Scalable Audio Feature Extraction

Devon Leno Bryant

A Thesis Presented to the Faculty of

University of Colorado at Colorado Springs

in Candidacy for the Degree of

Master of Science in Computer Science

Adviser: Dr. Rory Lewis

April 2014

c© Copyright by Devon Leno Bryant, 2014.

All rights reserved.

Recommended for Acceptance by

The Department of Computer Science

Dr. Rory Lewis Date

Dr. Jugal Kalita Date

Dr. Terrance Boult Date

Abstract

Feature extraction provides the foundation for many systems and research projects

dealing with machine learning. Extracting features from audio data can be compu-

tationally expensive and time consuming, particularly with larger sets of audio data.

This paper describes Safe, a system for scalable and efficient audio feature extrac-

tion in the Music Information Retrieval domain. The system builds upon previous

research in efficient audio feature extraction, taking advantage of both multicore pro-

cessing and distributed cluster computing to provide a scalable architecture for feature

extraction algorithms.

Lastly, the system’s performance is compared with current leading extraction li-

braries and a model for the system’s scalability is developed.

iv

Contents

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii

1 Introduction 1

2 Background and Related Work 3

3 System Description 6

3.1 Feature Representations . . . . . . . . . . . . . . . . . . . . . . . . . 6

3.2 System Interfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

4 Efficient and Scalable Extraction 11

4.1 Efficient Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . 11

4.2 Parallel Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . 14

4.3 Distributed Feature Extraction . . . . . . . . . . . . . . . . . . . . . 17

4.3.1 Localized Data . . . . . . . . . . . . . . . . . . . . . . . . . . 18

4.3.2 Distributed Data . . . . . . . . . . . . . . . . . . . . . . . . . 18

5 Evaluation and Results 21

5.1 System Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

5.2 Dataflow Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

5.3 Scalability Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

v

5.3.1 Multi-Processor Scalability . . . . . . . . . . . . . . . . . . . . 31

5.3.2 Multi-Node Cluster Scalability . . . . . . . . . . . . . . . . . . 32

6 Conclusions 37

6.1 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

A Implemented Features 39

A.0.1 Frame . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

A.0.2 Window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

A.0.3 Fast Fourier Transform . . . . . . . . . . . . . . . . . . . . . . 40

A.0.4 Power Spectrum . . . . . . . . . . . . . . . . . . . . . . . . . . 41

A.0.5 Constant-Q Transform . . . . . . . . . . . . . . . . . . . . . . 41

A.0.6 Mel-Frequency Cepstral Coefficients . . . . . . . . . . . . . . . 42

A.0.7 Spectral Shape . . . . . . . . . . . . . . . . . . . . . . . . . . 43

A.0.8 Spectral Flux . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

A.0.9 Onset Detection . . . . . . . . . . . . . . . . . . . . . . . . . . 45

B Evaluation Dataset 47

References 115

vi

List of Tables

4.1 Plan level parallelism . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

4.2 Feature level parallelism . . . . . . . . . . . . . . . . . . . . . . . . . 15

5.1 Evaluation Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

5.2 Evaluation Dataset Genre Statistics . . . . . . . . . . . . . . . . . . . 22

5.3 Extraction Libraries and Versions . . . . . . . . . . . . . . . . . . . . 22

A.1 CQT Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

A.2 MFCC Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

A.3 Spectral Shape Parameters . . . . . . . . . . . . . . . . . . . . . . . . 44

A.4 Spectral Flux Parameters . . . . . . . . . . . . . . . . . . . . . . . . 45

A.5 Spectral Onset Parameters . . . . . . . . . . . . . . . . . . . . . . . . 46

B.1 Full Evaluation Dataset . . . . . . . . . . . . . . . . . . . . . . . . . 48

vii

List of Figures

3.1 Safe Web Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

4.1 Sequential dataflow for MFCC, CQT, and Spectral Shape features . . 12

4.2 Optimal dataflow extraction plan . . . . . . . . . . . . . . . . . . . . 12

4.3 Safe Distributed Deployment . . . . . . . . . . . . . . . . . . . . . . . 20

5.1 Feature extraction times (1332 songs, 5120 minutes of audio) . . . . . 25

5.2 Feature extraction times for increasing number of songs . . . . . . . . 26

5.3 Feature extraction times for single audio files of increasing length . . 27

5.4 Performance increase from dataflow analysis (single-threaded) . . . . 28

5.5 Percentage of total time taken by individual sub-feature actors . . . . 32

5.6 Feature extraction processor speedup . . . . . . . . . . . . . . . . . . 33

5.7 Feature extraction processor scalability . . . . . . . . . . . . . . . . . 34

5.8 Feature extraction cluster/node scalability (local audio files) . . . . . 35

5.9 Feature extraction cluster/node scalability (HDFS distributed audio

files) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

A.1 Sequential dataflow of the Constant-Q transform . . . . . . . . . . . . 42

A.2 Sequential dataflow for Mel-Frequency Cepstral Coefficients . . . . . 43

A.3 Sequential dataflow for Spectral Shape . . . . . . . . . . . . . . . . . 44

A.4 Sequential dataflow for Spectral Flux . . . . . . . . . . . . . . . . . . 45

A.5 Sequential dataflow for Spectral Onsets . . . . . . . . . . . . . . . . . 46

viii

Chapter 1

Introduction

The Music Information Retrieval (MIR) domain spans a wide variety of disciplines

and applications. MIR techniques are being leveraged by businesses and academics for

recommendation systems, classification, identification, automatic transcription, simi-

larity searching, and more. Despite the seemingly different nature of these problems,

many of these systems actually leverage similar audio feature extraction algorithms

at their core. These feature extraction algorithms can range from low-level digital

signal processing techniques to high-level machine learning algorithms. Most of these

algorithms are computationally intensive, both in resource usage and running times.

The amount of audio data and music being produced today is significant. Com-

panies such as Spotify are adding over 20,000 songs per day to their existing catalog

of over 20 million songs [39]. The current Apple iTunes catalog has over 26 million

songs [33]. Most of the existing MIR libraries and algorithms are not easily scalable

to work with larger datasets such as these.

Numerous libraries exist for audio feature extraction and many have focused on al-

gorithmic optimizations for efficient extraction, often using C++ for its performance

benefits in numerical computing. However, as modern computing continues to shift

towards more parallel architectures [3], it is clear that different programming models

1

and techniques are needed to take full advantage of these systems. Often, the im-

perative techniques used for optimal execution on a single processor are ill-suited or

difficult to transition to parallel environments [40].

Additional research has been done in extending existing libraries to work in dis-

tributed computing environments [28, 15, 5]. These systems are all based on a mas-

ter/worker division of nodes, which suffers from two main drawbacks. The primary

drawback is that a master node in any system provides a single point of failure, mean-

ing these systems do not address issues with fault-tolerance. The second drawback is

that a master node in a system often enforces bounds on the scalability; introducing

an upper bound when throughput is limited by the master node and forcing a lower

bound of two nodes from the master/worker separation.

This paper describes a high-performance concurrent system for audio feature ex-

traction based on modern techniques in parallel computing and reactive program-

ming. Scalability of the system is fault-tolerant and addresses both vertical scalability

through multicore processing and horizontal scalability through distributed cluster-

ing. While the primary focus of this research is on performance and scalability, there

is a strong secondary goal towards providing a general extensible MIR framework for

others to re-use and build upon.

2

Chapter 2

Background and Related Work

There have been many MIR feature extraction libraries developed over the last decade,

each with different feature sets and focus. This section outlines some of the most

popular and relevant libraries in audio feature extraction, though many more exist.

One of the earliest libraries to provide audio feature extraction that is still used

today is Marsyas [41]. Marsyas is an efficient C++ framework for general dataflow

audio processing that has been used for various analysis and synthesis tasks. Marsyas

provides both a command-line interface and a graphical editor for building data flows.

Marsyas has also been extended to perform batch extraction in distributed environ-

ments [5]. The distributed model relies on a custom dispatcher node sending audio

clips over TCP to known worker nodes for feature extraction.

Yaafe [27] is a fast and efficient library for audio feature extraction written mostly

in C++ with a Python interface. The focus of Yaafe is on efficiency, utilizing dataflow

graphs to eliminate redundant calculations between different features. Yaafe is mostly

a command line tool and supports output in CSV or HDF5 formats.

jAudio [30] is a Java-based audio extraction framework developed primarily for

MIR researchers. The framework provides a GUI for selecting features and filling out

parameters. Results can be exported to XML or Weka’s ARFF format for further

3

experimentation or machine learning. jAudio also provides a mechanism for describ-

ing dependencies between features, similar to Yaafe, for the purpose of eliminating

duplicate calculations.

OMEN [28] is a distributed extraction framework that leverages jAudio as its base

extraction engine. The distribution model for OMEN is based on a hierarchy of differ-

ent node types: a master node provides the overall coordination, library nodes store

collections of audio data, and worker nodes perform the actual feature extraction. All

communication in OMEN is handled through SOAP-based web services.

Sonic Annotator [8] is a command-line tool for running batch feature extraction

on large numbers of audio files. Sonic Annotator is based on VAMP [19] plugins, a

general framework and C++ API for audio feature extraction algorithms. Numerous

VAMP plugin libraries have been developed by different research groups and are

available for use. Sonic Visualiser, a complimentary tool to Sonic Annotator, provides

a helpful user interface for feature exploration and many different graphical displays

for visualizing features. Unfortunately, algorithms and results in VAMP cannot be

shared between plugins which leads to some inefficiencies during extraction.

iSoundMW [15] is a distributed feature extraction framework and similarity search

engine. The distribution model uses a single master node that sends small segmented

frames of audio data to registered client worker nodes for feature extraction.

MIRtoolbox [26] is a Matlab-based MIR feature extraction library that takes ad-

vantage of existing visualizations and algorithms available in the Matlab ecosystem.

MIRtoolbox provides some explicit mechanisms for composing features in stages from

lower-level or intermediary calculations. However, memory management is an issue

since the entire file and all feature calculations are held in memory. MIRtoolbox pro-

vides some parallel extraction capabilities, allowing concurrent extraction of separate

files.

4

openSMILE [17] is a C++ based library focused on real-time Speech and MIR fea-

ture extraction. Batch extraction capabilities are provided as well as multi-threaded

support for parallel extraction on a single machine. openSMILE is based on a com-

plex shared data memory system which allows single-component writes and multi-

component reads of matrix data and leverages ring-buffers to manage memory. Dif-

ferent output formats are provided including CSV, ARFF, LibSVM, and HTK.

5

Chapter 3

System Description

3.1 Feature Representations

The primary abstraction for any extraction library is the description of a Feature.

Within the Safe system, each feature is described by a set of required and optional

parameters needed to perform its calculations. Features in audio processing are often

built on top of other features or share common steps in their calculations. For exam-

ple, the Fast Fourier Transform (FFT) is generally a shared step for any algorithms

working in the frequency domain. For that reason, each feature also describes its own

dataflow as a sequence of smaller computational steps. This dataflow representation

provides two major benefits. The first benefit is algorithm reuse; new algorithms can

leverage existing code for common calculations. The second benefit is that duplicate

calculations between features can often be eliminated through simple dataflow analy-

sis on the provided feature sets. Section 4.1 describes this dataflow analysis technique

in more detail. For a full list of implemented features, parameter descriptions, and

dataflow sequences see Appendix A.

Externally, features are described through a simple syntax similar to the syntax

used by Yaafe [27]. Listing 1 shows a simplified Backus-Naur Form (BNF) of the

6

feature extraction syntax used by the system and listing 2 shows an example entry of

this syntax. Each entry contains three primary sections:

1. name - A unique name for the feature extraction.

2. feature - The feature to be extracted and any feature parameters such as the

step size, window type, etc.

3. output - Optional output parameters such as the directory to write to or the

precision of the results.

Listing 1 Simplified Feature Syntax BNF

〈identifier〉 |= { A . . .Z | a . . . z | 0 . . . 9 }〈name〉 |= 〈identifier〉〈feature〉 |= 〈identifier〉

〈parameter〉 |= 〈identifier〉 ”=” ( 〈quoted string〉 | 〈number〉 )

〈parameters〉 |= 〈parameter〉 { ”,” 〈parameter〉 }〈feature definition〉 |= 〈name〉 ”:” 〈feature〉 [ 〈parameters〉 ] [ ”-out” 〈parameters〉 ]

Listing 2 MFCC Feature Extraction Description

mfcc: MFCC frameSize=256, stepSize=256, windowType=”hamming”

3.2 System Interfaces

Within the system, there are two primary interfaces for running feature extraction

over existing datasets. The first interface provides command-line execution for run-

ning bulk extraction on a single machine. Listing 3 outlines the syntax and parameters

used in the command-line interface.

The two required parameters for any extraction are:

7

Listing 3 Command Line Syntax

− i < f i l e > | −−input < f i l e >input path ( f i l e or d i r e c t o r y ) f o r audio f i l e ( s ) to p roce s s

−r | −−r e c u r s i v ef l a g to proce s s audio data in sub−d i r e c t o r i e s ( d e f a u l t = f a l s e )

−p < f i l e > | −−plan < f i l e >path to f e a t u r e e x t r a c t i o n plan f i l e

−f <value> | −−f e a t u r e <value>d e s c r i p t i o n and parameters f o r a s i n g l e f e a t u r e to e x t r a c t

−s <value> | −−sample−r a t e <value>sample ra t e in Hz o f a l l audio f i l e s ( d e f a u l t = 44100)

−o < f i l e > | −−output−d i r < f i l e >d i r e c t o r y to wr i t e f e a t r e output to ( d e f a u l t = ’ . / ’ )

−m | −−metr i c sf l a g to capture / p r i n t metr i c s ( d e f a u l t = f a l s e )

1. Input - The individual audio file or directory containing audio files to run ex-

traction on.

2. Plan File or Feature - A file containing the feature set and parameters or an

inline description of the feature and its parameters.

The remaining command-line parameters are optional and use the specified de-

faults if no input is provided. One particular parameter of interest is the sample

rate. Safe currently requires all audio data to be encoded with the same sample rate.

This limitation exists because several features use the expected sample rate to pre-

compute values for use in their calculations. For example, the Mel-Frequency Cepstral

Coefficients (MFCC) algorithm uses the sample rate along with other parameters to

pre-compute and cache its mel filter bank. In cases where a dataset contains audio

with different sample rates, the audio data needs to be pre-processed and re-sampled

into a common rate.

The Safe system also has the ability to capture and print out metrics for a given

extraction run. If enabled, Safe will capture the following metrics for every sub-feature

computation:

8

• Total Messages - The total number of times the sub-feature calculation was

called during extraction.

• Total Time - The accumulated total time spent calculating the sub-feature.

• Min, Max, Median, Mean Times - The minimum, maximum, median, and mean

times spent in a single calculation of the sub-feature.

The second system interface, used primarily for controlling extraction in dis-

tributed environments, is a web-based user interface. The web interface uses REST

[18] communication for executing extraction and monitoring the progress and system

status. Each extraction request is assigned a unique id that can be used for querying

the status of the extraction. There are three possible responses for extraction status

queries:

1. Ongoing - Specifies the number of audio files that have been processed along

with the total number of files.

2. Failed - Gives a description of any failures that occurred during extraction.

3. Finished - Specifies the total number of files processed and the time taken to

run the extraction (in milliseconds).

Figure 3.2 below shows a screenshot of the web interface for the system. This

example screenshot shows the three different status results for extraction requests.

9

Figure 3.1: Safe Web Interface

10

Chapter 4

Efficient and Scalable Extraction

The primary goal of this research is to provide an efficient framework for audio feature

extraction that can easily scale in both vertical and horizontal directions. This is

achieved by extending the techniques and algorithms used in other MIR libraries

and adapting them to work with functional, asynchronous and reactive programming

models. Safe is implemented in Scala [25] and leverages the Akka [2] library for

parallel and distributed computing.

4.1 Efficient Feature Extraction

As mentioned in chapter 3, features can often be described by dataflow sequences of

smaller computational steps or sub-features. Figure 4.1 shows an example of the step

sequences for calculating three different features: Mel-Frequency Cepstral Coefficients

(MFCC) [12], Constant-Q Transform (CQT) [6], and Spectral Shape [21].

By breaking down features into smaller steps, efficiencies can be gained by elim-

inating shared calculations. A set of multiple features in the system is combined

to form a Feature Plan. When shared calculations between feature sequences are

eliminated in a feature extraction plan, the plan naturally forms a directed acyclic

dataflow graph. This is a common technique in dataflow analysis that is used by other

11

MIR feature extraction libraries [29, 27] to eliminate redundant calculations. This

technique is similar to the problem of Common Subexpression Elimination [10] in

compiler optimization. Figure 4.2 below shows an example of the optimized dataflow

extraction plan for the same three features described earlier.

Audio in Frame Window FFT Mag MFCC

Audio in Frame Window FFT CQT

Audio in Frame Window FFT MagSpectralShape

Figure 4.1: Sequential dataflow for MFCC, CQT, and Spectral Shape features

CQT

FFT

Mag

MFCC

SpectralShape

WindowFrameAudio in

Figure 4.2: Optimal dataflow extraction plan

Feature extraction plans in Safe are encoded using adjacency lists. Each plan holds

a single feature or sub-feature computation and zero or more downstream plans to

pass results to. Algorithm 1 shows the basic data representation for feature extraction

12

plans. Algorithm 2 shows the heuristic algorithm that is used to combine multiple

features or plans into optimal dataflow graphs.

Algorithm 1 Feature Extraction Plan Representation

case class Plan(feature: Feature, children: Seq[Plan])

Algorithm 2 Dataflow Graph Optimization

def dataflow(plans: Seq[Plan]): Seq[Plan] =

plans.foldLeft(Nil)((merged, plan) => merge(merged, Seq(plan)))

def merge(as: Seq[Plan], bs: Seq[Plan]): Seq[Plan] = {

val merged = for {

a <- as

b <- bs if (a.feature == b.feature)

} yield Plan(a.feature, merge(a.children, b.children))

(as ++ bs).filterNot(

p => merged.exists(_.feature == p.feature)) ++ merged

}

13

4.2 Parallel Feature Extraction

With the processing model expressed as a directed acyclic dataflow graph, audio data

can be streamed through the pipeline to extract the various features. This approach

lends itself to numerous levels of parallelism:

• File Level - Extraction can be run concurrently on multiple files.

• Plan Level - Any time a plan branches into separate paths, each path can run

concurrently on the results of the parent calculation.

• Feature Level - When audio data is split into separate chunks (frames), each

stage of a feature extraction sequence can concurrently operate on a different

section of the data.

File level parallelism is a fairly self-explanatory concept and is the approach used

in [26]. It is the most obvious form of parallelism and requires little to no coordination

between processes. Tables 4.1 and 4.2 show examples of plan level and feature level

parallelism where each row represents a different processor and each column repre-

sents possible computations over time. With plan level parallelism, different steps can

run concurrent calculations on the same data outputs of a parent step. For example,

table 4.1 shows that the CQT and Magnitude calculations can run concurrently on

the output of the FFT. With feature level parallelism, a given calculation may be

executing on a section of audio data concurrently with downstream calculations run-

ning off previous results. In table 4.2, the example highlights that a Window function

can pass its output to the FFT function and immediately begin working on the next

frame. Note: These tables are used for illustrative purposes of where parallelism can

occur and do not represent the actual thread scheduling and division within the sys-

tem. In the actual running system, some steps will take much longer than others and

a single thread may finish multiple steps in the time another thread takes to execute

14

one. Additionally, different combinations of file, plan, and feature level concurrency

may occur together.

FFT(window1) Magnitude(fft1) MFCC(mag1)CQT(fft1) Spectral Shape(mag1)

Table 4.1: Plan level parallelism

frame1 Window(frame1) FFT(window1) CQT(fft1)frame2 Window(frame2) FFT(window2)

frame3 Window(frame3)frame4

Table 4.2: Feature level parallelism

Managing these different levels of concurrency with traditional thread scheduling

and lock management would be complicated and difficult. Instead, an Actor model

[1] for concurrency is used.

Actors are objects that encapsulate state and behavior and communicate exclu-

sively through messaging. Messaging between actors is never direct. Instead, com-

munication occurs through the use of recipient mailboxes. Each actor has exactly one

mailbox and messages are dequeued and processed by the actor in a serial fashion.

This serial processing guarantee helps eliminate synchronization and locking concerns

within the actor’s actual processing logic. Conceptually, actors can be thought of as

having their own light-weight isolated threads. It is the responsibility of the actor

container to manage the actual scheduling and execution against real threads.

Actors also provide a rich hierarchy and supervision system. Each actor can create

any number of child actors to execute sub-tasks. When an actor creates another actor,

it becomes a supervisor and is responsible for delegation and control of the child actor.

When a child actor fails for any reason, the supervisor (parent) is notified of the failure

and is responsible for handling the failure. When a subordinate fails, the supervising

actor has the option to resume the subordinate (keeping any existing state), restart the

15

subordinate (clearing out any previous state), terminate the subordinate, or escalate

the failure (failing itself).

Actor models, being asynchronous and message-based, provide a natural encoding

for stream based dataflow processing. Within Safe, each computational step in the

dataflow graph is represented by an actor and the intermediary results (messages)

between actors are immutable. By encoding the feature extraction dataflow graphs

as actor hierarchies in this fashion, all three levels of parallelism mentioned (file, plan,

and feature) are provided with no additional components required. The dynamic

nature of Akka’s actor system make it simple to create complex fault-tolerant feature

extraction hierarchies. Within a feature extraction plan, each actor usually takes on

one of four common messaging roles described in [24]:

• Splitter - Actors that take a single message or result and split the work into

smaller pieces for downstream processing. Splitters provide much of the par-

allelism in feature extraction. File level parallelism is provided by splitting a

message with multiple file locations (or a directory) into individual messages

for each file. Feature level parallelism is provided by a Frame actor, splitting

an individual file into smaller chunks (frames) to perform further computations

on.

• Transform - The most common actor type in a feature extraction plan is a

transform actor. These actors provide simple mapping functions from one type

to another and send the result on for further processing. Examples of transforms

are Windowing functions, FFT, Power Spectrum, MFCC, Spectral Shape, etc.

• Resequencer - Actors that take a stream of potentially unordered messages and

send them out in an ordered sequence. Sequencing is important when writing

out frame-based extracted features. For example, when writing out to a CSV

16

file the features need to be in the same order as the frames in the source audio

file.

• Aggregator - Aggregators are used to calculate features that go across multiple

frames in an audio file or across an entire dataset. Some example aggregate

functions are Spectral Flux or any max/min/average/total/etc. calculation

across audio files.

By default, a single actor is created for each node in the dataflow graph and the

actor messaging hierarchy mirrors the arcs in the graph. For many systems, the con-

currency provided by the file, plan, and feature level divisions is sufficient. However,

for highly concurrent systems (systems with a large number of processors), the Split-

ter and Transform actors can be replaced with actor pools and a round-robin routing

strategy. This strategy allows concurrent execution of the same computational step on

different frames. For example, multiple FFT calculations could be run concurrently

on different frames. In the current system, Resequencers and Aggregators cannot be

placed into pools because they need to maintain a temporary state across multiple

messages. Because of this requirement, scalability is often limited by features that

require aggregate calculations.

4.3 Distributed Feature Extraction

The distribution model for the system leverages Akka’s clustering framework [9].

Akka’s cluster system provides a fault-tolerant decentralized membership service

based on Amazon’s Dynamo [14] store. The cluster architecture uses gossip pro-

tocols [42] to manage node membership within the cluster. Using the gossip protocol,

the current state of the cluster is gossiped between nodes and preference is given to

members that have not yet seen the latest version. Initiation into a cluster is handled

by a node sending a join message to one of the existing nodes in the cluster. In order

17

to provide a fault-tolerant solution, failures in the cluster need to be detected and

communicated to the rest of the system. For this, Akka uses a Phi Accrual Failure

Detector [23].

While previous research in distributed audio feature extraction exists [28, 15, 5],

they are all based on a master/worker division of nodes. By leveraging a decentralized

and masterless clustering framework, there is no single point of failure or single point

bottlenecks in the system. Since the clustering framework is Actor based as well, it

is simple to scale the feature extraction framework from a single machine to multiple

nodes.

4.3.1 Localized Data

The simplest form of distributed feature extraction assumes that a collection of audio

files is already distributed across the cluster onto the local file systems. This config-

uration is similar to the library node concept used by OMEN [28]. From there, bulk

extraction can be performed across the cluster through a simple broadcast message

to all nodes in the cluster. While this provides a simple and scalable solution, this

approach is not fault-tolerant or flexible. This distribution model couples the extrac-

tion execution to the machines containing the data. If a node is down or fails, there

is no way to run extraction over the audio data stored on that node.

In this case, the time taken to extract all features will be limited by the longest

running node in the cluster. For optimal extraction times, the data needs to be evenly

balanced across the cluster and nodes within the cluster should be homogeneous, i.e.

each machine should have an equal number of computing resources.

4.3.2 Distributed Data

A more robust and fault-tolerant model for distributed extraction requires a dis-

tributed file system such as Hadoop HDFS [38]. Hadoop provides a whole suite of

18

capabilities for distributed analysis and transformation of data using the MapReduce

[13] paradigm. However, Safe’s cluster extraction only leverages the distributed file

system from Hadoop.

In HDFS, there are two types of nodes: NameNodes and DataNodes. NameN-

odes provide the overall management and coordination of directory hierarchies and

location of the data within HDFS. File content is split into chunks, typically 128 mb

in size, and those chunks are stored on the local file systems of DataNodes. Fault-

tolerance in HDFS is provided through replication. Individual chunks are replicated

and distributed onto multiple DataNodes. In a standard configuration NameNodes in

a hadoop cluster provide a single point of failure, though a High Availability (HA) fea-

ture is provided that allows redundant NameNodes to be configured for fast failover.

HDFS clients typically access files through a TCP protocol, but a short-circuiting

mechanism [34] is provided for cases when the data and processing are co-located on

the same machine.

Figure 4.3.2 shows the distributed deployment architecture of the system using

HDFS. Since the Safe cluster is masterless, any number of nodes in the cluster can

be configured to serve http requests. Safe nodes can be co-located with HDFS nodes

or run on separate clusters.

When an extraction request is made to one of the nodes in the Safe cluster, the

receiving node will query the NameNode for all the paths of the audio data and route

extraction of individual audio files to different nodes in the cluster. The primary

routing logic uses a Round-Robin routing strategy to distribute the extraction work-

load across the cluster. Some experimentation with routing strategies that take data

locality into account was also performed. In this case, the routing logic favors sending

extraction requests to the same machine where the data is located, taking advantage

of the HDFS short-circuit reads. When different nodes in a cluster are unbalanced,

i.e. some nodes have a larger heap, more CPU, etc., an adaptive load balancing router

19

Figure 4.3: Safe Distributed Deployment

should be used instead to distribute feature extraction. Akka provides a probabilistic

router based on weighting from the following metrics:

• Heap - Remaining JVM heap capacity.

• Load - System load average over the past minute.

• CPU - Percentage of CPU utilization.

20

Chapter 5

Evaluation and Results

5.1 System Evaluation

In order to evaluate the system’s performance and scalability, a large dataset of music

audio was required. The dataset used for testing was a private collection of 1,332

songs, approximately 5,120 minutes of audio. All files were encoded in CD-quality

44.1 KHz 16-bit stereo WAV format. The overall dataset was split into smaller subsets

to allow for testing on varying sized datasets. Tables 5.1 and 5.2 show some general

statistics about the datasets used in testing. For a full listing of all the songs, see

Appendix B.

Safe was compared with three other feature extraction libraries for performance:

Yaafe, openSMILE, and Sonic Annotator. Yaafe and openSMILE in particular were

chosen because of their strong performance characteristics. Table 5.3 shows the ver-

sions of each library used for this evaluation. Initial evaluations also included MIR-

Toolbox, though its performance was significantly worse and the results have been

left out of these benchmarks.

21

Audio Files Total (minutes) Max (minutes) Min (minutes) Avg (minutes)1 2.5 2.5 2.5 2.52 5.0 2.5 2.5 2.54 10 3.49 1.51 2.59 20 4.04 1.09 2.2217 40 4.56 0.92 2.3535 80 4.56 0.59 2.2961 160 5.91 0.59 2.62107 320 6.98 0.41 2.99200 640 11.35 0.41 3.2371 1280 11.35 0.41 3.45707 2560 15.72 0.23 3.621332 5120 32.25 0.11 3.84

Table 5.1: Evaluation Dataset

Genre Song CountProgressive Metal 19

Folk Rock 2Pop Rock 3New Wave 32Indie Rock 223

Metal 79Jazz 171Punk 386Swing 22

Stoner Metal 27Alt-Country 10

Folk 30Rockabilly 3

Rock 31Alternative 213

Blues 6Hip-Hop 74

Table 5.2: Evaluation Dataset Genre Statistics

Library VersionYaafe 0.64openSMILE 2.0-rc1Sonic Annotator 1.0Safe 0.1

Table 5.3: Extraction Libraries and Versions

22

The benchmark used for comparing the different libraries was a measure of the

total time taken to extract six different features and output the results to CSV files.

The six features picked for the benchmark were:

• Mel-Frequency Cepstral Coefficients (13 coefficients, 40 filters)

• Spectral Flux

• Spectral Centroid

• Spectral Spread

• Spectral Skewness

• Spectral Kurtosis

This particular feature set was chosen because each library has support for these

features and the algorithms are fairly standard and not likely to vary between imple-

mentations. For all features, the frame size was 1024 samples and the step (hop) size

was 512. All of the scripts and configuration used for this evaluation are available

online [37]. In the interest of fairness, attempts were made to provide configurations

for optimal performance with each library.

Yaafe was compiled with FFTW [20] for fast FFT calculations. Since Yaafe per-

forms dataflow analysis to optimize execution, no other special configuration was

required. Since openSMILE has support for multi-threaded concurrency, experimen-

tation was performed to find an optimal number of threads for the test. Ultimately,

openSMILE was configured to run with a single thread because all tests with multi-

threaded configurations appeared to degrade the performance. For openSMILE, the

dataflow graph is encoded explicitly into the configuration since there is no dataflow

analysis support. For Sonic Annotator, the LibXtract [7] plugins were used to calcu-

late the spectral shape features (centroid, spread, skewness, and curtosis), while the

23

BBC plugins [11] and Queen Mary University plugins [32] respectively were used to

calculate the Spectral Flux and MFCC features. Sonic Annotator does not provide

any support for dataflow optimizations, so many of the algorithms are running re-

dundant calculations. For this benchmark Safe was configured using Akka’s default

Fork-Join executor, which calculates its thread pool size based on the number of

processors p and a parallelism factor ν (default 3.0):

poolsize = dpνe (5.1)

All evaluations were run on a Macbook Pro, OS X 10.7.5, with a 2.5 GHz Intel

Core i7 processor 8 GB 1333 MHz DDR3 RAM. For each evaluation, the mean time

and standard deviation were calculated from ten iterations. The standard deviation

for each library was relatively small. In many of the plots, the standard deviation is

left out because the bars are indistinguishable from the plotted points.

Figure 5.1 shows the overall time taken by each library to run extraction over the

full dataset. Figure 5.2 shows a plot of each libraries performance as the number

of songs increases. Yaafe, openSMILE, and Safe (single-threaded) are all very close

in terms of performance. In this benchmark, the multi-threaded version of Safe is

roughly four times faster than the other solutions.

24

Sonic Annotator Yaafe openSMILE Safe-1 Safe

20

40

60

80

100

120112.39

89.24 88.09

79.81

23.56

Extr

acti

onT

ime

(min

ute

s)

Figure 5.1: Feature extraction times (1332 songs, 5120 minutes of audio)

In order to verify that the parallelism in Safe is not limited to concurrent extraction

of multiple files, a benchmark was setup to measure extraction times on single audio

files of increasing length. Figure 5.3 shows the results of this benchmark. For this

benchmark, Safe was configured to use Akka’s fixed thread pool dispatcher with five

threads. These results indicate that the Safe system has a more significant fixed

startup cost than the other libraries but better variable performance with increasing

recording lengths.

25

101 102 103 104

10−1

100

101

102

Minutes of Audio

Extr

acti

onT

ime

(min

ute

s)

openSMILEYaafe

Sonic AnnotatorSafe

Figure 5.2: Feature extraction times for increasing number of songs

26

1 2 3 4 5 6 7 8 9 100

2

4

6

8

10

12

14

Minutes of Audio

Extr

acti

onT

ime

(sec

onds)

openSMILEYaafe

Sonic AnnotatorSafe

Figure 5.3: Feature extraction times for single audio files of increasing length

27

5.2 Dataflow Analysis

In order to test the benefits of dataflow analysis, Safe’s performance was measured

both with and without dataflow optimizations. Testing was performed using the

same dataset and feature set described previously in section 5.1. For the sequential

case, each audio file was loaded only once but the individual extraction algorithms

were run independently with no shared calculations. This setup is very similar to the

way many feature extraction libraries (including Sonic Annotator) work. Figure 5.4

shows the extraction times both with dataflow optimizations and without (sequential).

For this particular feature set, we see a 33% decrease in the overall extraction time

when dataflow optimizations are applied. However, the benefits can vary significantly

depending on the selected feature sets and input parameters.

0 20 40 60 80 100 120 140 160

0

20

40

60

80

100

120

140

160

180

200

220

Minutes of Audio

Extr

acti

onT

ime

(sec

onds)

SequentialDataflow

Figure 5.4: Performance increase from dataflow analysis (single-threaded)

28

5.3 Scalability Analysis

The techniques used to measure and describe scalability of the system are all based

on Gunther’s Universal Scalability Law (USL) [22]. Within this model, one of the

primary measurements for parallelism in a system is the system’s speedup factor.

Speedup is defined as the ratio of elapsed time on a single processor T1 to the elapsed

time on p processors Tp.

S(p) =T1Tp

(5.2)

Speedup measurements are taken by executing a fixed workload on increasing

numbers of processors (or machines). The result is a quantification of the reduced

execution time for adding more processors to the system. Another important measure-

ment of a system’s parallelism, based on speedup, is efficiency. Efficiency measures

the average speedup per processor, which is defined as:

E(p) =S(p)

p(5.3)

In theory, a system that is perfectly parallel will have linear speedup T1/p. In

practice, however, there are other factors that make linear speedup in systems un-

realistic. The Universal Scalability Model was developed to take these additional

factors into account and give a realistic description of a systems true scalability. The

two primary factors involved in this model are:

• Contention (σ) - The serial fraction of the total execution. This represents the

portion of an algorithm that cannot be split up and needs to run serially.

• Coherency (κ) - The additional overhead of managing extra processors and

interprocessor communication.

29

Taking these factors into account, the Universal Scalability equation for system

capacity C(p) is defined as:

C(p) =p

1 + σ(p− 1) + κp(p− 1)(5.4)

Equation 5.4 describes a concave function. Under this model, most systems will

have a maximum capacity at p∗. Adding more processors beyond p∗ will only degrade

performance. The maximum capacity can be obtained through the following equation:

p∗ =

⌊√1− σκ

⌋(5.5)

The σ and κ parameters in the USL are calculated by performing regression anal-

ysis on the measured system throughput X(p). The full procedure for calculating

these parameters is outlined in [22].

All scalability testing of the system was performed on the University of Colorado

at Colorado Springs virtual cloud infrastructure [31] running a VMWare vSphere

5 hypervisor. The particular cluster used consisted of 8 physical HP machines w/

2.4GHz Intel Xeon E5530 CPUs. The total available resources in the cluster consisted

of:

• 64 Processor Cores

• 153 GHz total CPU

• 512 GB Memory

• 55 TB Connected Storage

All virtual machines used in testing were running a 64-bit CentOS 6.4 operating

system.

30

5.3.1 Multi-Processor Scalability

In addition to providing a simple model for developing concurrent applications, the

Actor model provides a simple theoretical foundation for estimating scalability. Since

each actor processes messages from its inbox serially, the contention factor σ in an

actor system can be estimated by running the system with a single processor (or

single-threaded) and measuring the percentage of time spent in each actor. The

largest percentage of time spent in a single actor should be very close to the overall

contention factor.

Using the same features and dataset described in section 5.1, figure 5.5 below shows

the percentage of time spent computing each step (sub-feature) for different pool sizes.

In the case where a single actor is dedicated to each sub-feature computation (pool

size of 1), reading in the input audio and creating the frames takes approximately 21%

of the time on a single processor. Therefore, the theoretical serial contention factor

σ1 should be ≈ 0.21. In the current system, features that require aggregation (such as

Spectral Flux) cannot be pooled and only a single actor is created regardless of pool

size. Therefore, these features will often become the dominant factor in scalability

when pooling is used. With this feature set, the Spectral Flux calculation takes

approximately 13% of the time on a single processor. For pool sizes of two or more,

the contention factor σn should be ≈ 0.13.

In order to test the system’s actual multi-processor scalability, the same extraction

test was run on virtual machines with an exponentially increasing number of single-

core processors p = 1, 2, 4, 8, . . . , 64. Multiple tests were run to capture the impact

of increasing actor pool sizes. Figure 5.6 shows the measured system speedup. As

expected from the theoretical model, no performance increases were seen for pool

sizes greater than two.

Figure 5.7 shows the measured throughput and modeled curve fits based on the

USL calculations. From these equation parameters, our maximum capacity p∗ for

31

Fr (I/O) FFT MFCC Shape Flux CSV (I/O)

2

4

6

8

10

12

14

16

18

20

22 21

16

12

10

13

6

12

87

5

13

6

9

5 54

13

6

%O

fE

xtr

acti

onT

ime

Pool 1 Pool 2 Pool 3

Figure 5.5: Percentage of total time taken by individual sub-feature actors

this particular feature set is 20 processors for a pool size of one (single actor per

sub-feature computation) and 28 processors for a pool size of two. The calculated

contention factors σ1 = 0.203 and σ2 = 0.123 were very close to the theoretical

expected values of 0.21 and 0.13.

5.3.2 Multi-Node Cluster Scalability

The same USL model used to evaluate Safe’s multi-processor scalability was used

for measuring the distributed cluster scalability. Instead of increasing the number of

processors on a single virtual machine, tests were run on increasing numbers of virtual

machines. Each node (VM) in the cluster was given the same number of resources

32

0 10 20 30 40 50 60 70

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

Number of Processors

Sp

eedup

Fac

torS

(p)

Pool Size 1Pool Size 2

Figure 5.6: Feature extraction processor speedup

(processors, disk space, etc.). In most hosted VM environments, multiple VM’s will

often share and compete for physical resources such as disk space and processors. In

an attempt to minimize this contention for these tests, each VM was mapped to a

separate physical hard drive and processor. Since the physical cluster only provided

six separate disk partitions and eight separate processors, the maximum number of

nodes tested was six.

The first test, as outlined in section 4.3.1, relied on the dataset being distributed

evenly across the cluster onto the local drives. For this test, the audio files were

distributed across the cluster and placed in the same directory location on each node.

Communication between nodes in this configuration is minimal and only relies on

the initial broadcast message to perform extraction and the final aggregation step

determining when all nodes have finished.

33

0 10 20 30 40 50 60 70

40

60

80

100

120

140

160

180

200

σ1 = 0.203, κ1 = 0.001949

σ2 = 0.123, κ2 = 0.001101

Number of Processors

Extr

acti

onT

hro

ugh

put

(sec

ofau

dio

/se

c)

Pool 1 (measured)Pool 1 (USL model)Pool 2 (measured)

Pool 2 (USL model)

Figure 5.7: Feature extraction processor scalability

Figure 5.8 shows the measured and USL calculated scalability of this model. As

expected, because of the minimal coordination between nodes, the scalability is close

to linear. Based on the calculated contention and coherency, the maximum capacity

p∗ of this configuration is 82 nodes.

While the previous localized data configuration has close to ideal scalability, it is

a poor choice for fault-tolerance or flexibility in the distribution model. The second

test, as described in 4.3.2, relied on the audio data being distributed through HDFS.

The HDFS configuration for these tests specified a replication factor of 3 and a block

size of 128 MB. For CD quality wav files (44.1 KHz, 16-bit stereo), 1 minute of

audio is approximately equal to 10 MB. Therefore, most songs under 12 minutes in

length were stored in a single block. Each node ran an instance of the Safe software

34

1 2 3 4 5 61

2

3

4

5

6

7

8

9

10

σ = 0.0113, κ = 0.000144

Number of Nodes

Extr

acti

onT

hro

ugh

put

(min

ofau

dio

/se

c)

MeasuredUSL model

Linear

Figure 5.8: Feature extraction cluster/node scalability (local audio files)

and doubled as an HDFS data node. With each new node added, the cluster was

re-balanced to more evenly distribute the audio files.

Initial testing leveraged a cluster-aware router that attempted to route extraction

of individual files based on data locality. In this case, preference was given to nodes

where the physical audio files and processing were co-located in an attempt to take

advantage of HDFS short-circuit local reads. However, testing revealed that the

HDFS re-balancing algorithm did not provide a truly even distribution of the data,

causing the distributed workload to become unbalanced. Instead, a standard round-

robin routing strategy was used which gave better overall performance.

Figure 5.9 below shows the measured and USL calculated scalability of the HDFS-

based distribution. Not surprisingly, the contention and coherency factors are slightly

higher in this configuration, but the fault-tolerance and flexibility make this a more

35

appealing option for most systems. With this model, the maximum capacity p∗ is 28

nodes.

1 2 3 4 5 61

2

3

4

5

6

7

8

9

10

σ = 0.0133, κ = 0.001234

Number of Nodes

Extr

acti

onT

hro

ugh

put

(min

ofau

dio

/se

c)

MeasuredUSL model

Linear

Figure 5.9: Feature extraction cluster/node scalability (HDFS distributed audio files)

36

Chapter 6

Conclusions

Many of the current frameworks for audio feature extraction have focused on algo-

rithms and optimization techniques for serial execution. Dealing with larger datasets

requires a more scalable model for extraction. In this paper, the Safe system for

high-performance concurrent audio feature extraction was outlined. In comparison

benchmarks with current leading MIR extraction libraries, Safe outperformed the

existing solutions for all datasets with more than 3 minutes of audio.

Scalability analysis provided models for the system’s multi-processor and multi-

node cluster scalability. The actor model of concurrency used in Safe provides a simple

theoretical model for predicting the vertical (processor) scalability with any given

feature set. Further scalability testing demonstrated that the measured contention

factors lined up with the expected theoretical values.

Two different models for multi-node cluster scalability were evaluated. The first

model relied on the dataset being distributed across the cluster onto the individual

node’s local file systems. The second, more flexible and fault-tolerant model, lever-

aged a true distributed file system. Using the Universal Scalability Law to estimate

the system’s contention and coherency factors, the local file system based approach

37

was shown to have close to ideal scalability. Although slightly less performant, the

system’s scalability using the distributed file system was still very strong.

All of the source code for Safe is available online [35] and released under the

Eclipse Public License version 1.0 [43].

6.1 Future Work

In addition to adding more algorithms and features to Safe, there are certain areas

where scalability of the system can be improved. Many of the current aggregation

features in the system are un-necessarily limited to a single actor. In cases where

aggregation is performed on a per-file basis, a smarter routing component can use the

origin file information to appropriately route messages. More specifically, a consistent

hashing router can be used that takes the origin file’s path as the hash key.

All of the cluster scalability tests were performed on a homogenous cluster. Ad-

ditional research into smarter distributed routing for unbalanced or inhomogeneous

clusters would help in optimizing extraction times. Akka provides an adaptive prob-

abilistic load balancing router that can be leveraged or extended for this task.

Lastly, future enhancements should address more aspects of fault-tolerance within

the system. While minimal support for fault-tolerance is built into the system through

the libraries being used, additional logic needs to be incorporated for handling failures

during extraction. Ideally, the extraction system should be able to recover from node

failures or network partitions.

38

Appendix A

Implemented Features

A large portion of this research consists of analyzing MIR feature extraction algo-

rithms and breaking them down into smaller sequential components for dataflow

analysis. The following sub-sections describe the various feature extraction algo-

rithms implemented in the system, along with any common or shared sub-feature

computations.

A.0.1 Frame

The framing function produces an iterator of fixed-size, potentially overlapping,

frames from an audio input stream. Each frame is normalized based on the encoding

bit-depth and multiple channels are combined to produce a single frame of normalized

audio. The following algorithm shows the combination for a single sample S based

on the number of channels n and bit-depth d:

S =

∑ni=1 si

(2d − 1)n(A.1)

39

A.0.2 Window

Safe provides a common set of windowing functions, including Hann (A.2), Hamming

(A.3), Blackman (A.4), Blackman-Harris (A.5), and Bartlett (A.6) windows.

w(n) = 0.5(

1− cos(

2πn

L− 1

)), 0 ≤ n < L (A.2)

w(n) = 0.54− 0.46 cos(

2πn

L− 1

), 0 ≤ n < L (A.3)

w(n) = 0.42− 0.5 cos(

2πn

L− 1

)+ 0.08 cos

(4π

n

L− 1

), 0 ≤ n < L (A.4)

w(n) = a0 − a1 cos( 2πn

L− 1

)+ a2 cos

( 4πn

L− 1

)− a3 cos

( 6πn

L− 1

), 0 ≤ n < L

a0 = 0.35875, a1 = 0.48829, a2 = 0.14128, a3 = 0.01168 (A.5)

w(n) =

2nL−1 0 ≤ n ≤ L−1

2

2− 2nL−1

L−12≤ n < L

(A.6)

A.0.3 Fast Fourier Transform

The FFT algorithm used in Safe is a JVM-optimized version of the standard Cooley-

Turkey algorithm. The standard Cooley-Turkey algorithm is described as:

Xk =

N/2−1∑m=0

x2me−2πiN/2

mk + e−2πiN

k

N/2−1∑m=0

x2m+1e− 2πiN/2

mk (A.7)

40

Further information on the implementation and optimization techniques used can

be found online [36].

A.0.4 Power Spectrum

Safe provides various spectrum functions, including Power P (k), Magnitude A(k),

and Phase φ(k). Each function is based on the spectral coefficients ak and bk computed

by the FFT.

P (k) = a2k + b2k (A.8)

A(k) =√a2k + b2k (A.9)

φ(k) = tan−1bkak

(A.10)

A.0.5 Constant-Q Transform

The Constant-Q transform (CQT) is similar to the Fourier transform, but uses log-

arithmic spacing instead of linear spacing. This is beneficial in audio analysis since

notes in western music are spaced logarithmically. The basic CQT algorithm is given

by:

Xk =1

Nk

Nk−1∑n=0

wk,nxne−j2πQnNk (A.11)

The CQT algorithm used in Safe is based on the Brown and Puckette [6] algorithm,

which leverages the standard FFT algorithm for faster computations. Figure A.1

shows the dataflow for the CQT algorithm.

41

Audio in Frame Window FFT CQT

Figure A.1: Sequential dataflow of the Constant-Q transform

Name Description Default Value

sampleRate Target (expected) sample rate of audio inputs 44100stepSize The step size (number of samples) for the fram-

ing function512

windowType Windowing function (bartlett, blackman, black-manHarris, hamming, hann)

hann

binsPerOctave The number of CQT bins/octave (default 24 =quarter-tone spacing)

24

maxFreq Maximum frequency (hz) to look for (default isG10, MIDI note 127)

12543.854

minFreq Minimum frequency (hz) to look for (default isC1, MIDI note 12)

16.351599

threshold Minimum threshold 0.0054

Table A.1: CQT Parameters

A.0.6 Mel-Frequency Cepstral Coefficients

The mel-frequency cepstrum provides a description of an audio signals power spectrum

based on the mel scale, a perceptually driven frequency scale. In this scale, the mel

representation m of a frequency f (hz) is given by:

m = 1127 loge

(1 +

f

700

)(A.12)

The MFCC computation is based on the Davis and Mermelstein [12] algorithm,

which is described as:

Yk =

N/2−1∑f=0

A(k)wk(f) (A.13)

MFCCn =K−1∑k=0

log Yk cos

((2k + 1)nπ

2K

)

42

Where Yk represents the application of K overlapping triangular (mel-scale

spaced) filters on the magnitude spectrum of the signal A(k) and n number of MFCC

coefficients are calculated through a discrete-cosine transform on the log-energy

outputs of Yk. Figure A.2 shows the dataflow for the MFCC algorithm.

Audio in Frame Window FFT Mag MFCC

Figure A.2: Sequential dataflow for Mel-Frequency Cepstral Coefficients

Name Description Default Value

sampleRate Target (expected) sample rate of audio inputs 44100frameSize Frame size (number of samples) for the framing

function1024

stepSize Step size (number of samples) for the framingfunction

512

windowType Windowing function (bartlett, blackman, black-manHarris, hamming, hann)

hann

numCoeffs Number of cepstral coefficients to extract 13melFilters Number of mel filter banks to use 40minFreq Minimum frequency (hz) for the filter bank 130.0maxFreq Maximum frequency (hz) for the filter bank 6854.0

Table A.2: MFCC Parameters

A.0.7 Spectral Shape

Spectral shape is an aggregate of four separate spectral features: Centroid, Spread,

Skewness, and Kurtosis. The calculations for these features are described in [21], and

are defined by the following equations:

Scentroid = µ1 (A.14)

Sspread =√µ2 − µ2

1 (A.15)

43

Sskewness =2µ3

1 − 3µ1µ2 + µ3

S3spread

(A.16)

Skurtosis =−3µ4

1 + 6µ1µ2 − 4µ1µ3 + µ4

S4spread

− 3 (A.17)

Where µi =∑N−1k=0 kiA(k)∑N−1k=0 A(k)

and A(k) represents the magnitude spectrum of the signal.

Figure A.3 shows the dataflow sequence for the spectral shape algorithm.

Audio in Frame Window FFT Mag Shape

Figure A.3: Sequential dataflow for Spectral Shape

Name Description Default Value

sampleRate Target (expected) sample rate of audio inputs 44100frameSize Frame size (number of samples) for the framing

function1024

stepSize Step size (number of samples) for the framingfunction

512

windowType Windowing function (bartlett, blackman, black-manHarris, hamming, hann)

hann

Table A.3: Spectral Shape Parameters

A.0.8 Spectral Flux

The spectral flux algorithm is based on the algorithm for measuring change in the

power spectrum described by Dixon in [16]. The algorithm computes the flux between

consecutive frames (n, n − 1) as the summation of the positive differences between

the magnitude at each frequency bin, as shown in the following equation.

SF (n) =N−1∑k=0

H(|X(n, k)| − |X(n− 1, k)|

)(A.18)

44

In this equation, H represents a half-wave rectifier function where H(x) = x+|x|2

.

Figure A.4 below shows the dataflow for the spectral flux extraction algorithm.

Audio in Frame Window FFT Mag Flux

Figure A.4: Sequential dataflow for Spectral Flux

Name Description Default Value

sampleRate Target (expected) sample rate of audio inputs 44100frameSize Frame size (number of samples) for the framing

function1024

stepSize Step size (number of samples) for the framingfunction

512

windowType Windowing function (bartlett, blackman, black-manHarris, hamming, hann)

hann

diffLength Compare frames spaced n length apart, 1 = con-secutive frames

1

Table A.4: Spectral Flux Parameters

A.0.9 Onset Detection

Safe implements the spectral onset detection described in [4]. The algorithm extends

the standard spectral flux calculation by applying a logarithmically spaced filter-bank

(similar to the Constant-Q transform spacing). The onset detection function is given

by:

O(n) =

k=N/2∑k=1

H(|X log

filt(n, b)| − |Xlogfilt(n− 1, b)|

)(A.19)

Peaks in the onset detection function are selected if all of the following conditions

are satisfied:

1. ODF (n) = max(ODF (n− w1 : n+ w2)

45

2. ODF (n) ≥ mean(ODF (n− w3 : n+ w4)) + δ

3. n− nlast onset > w5

Where δ is a fixed threshold value and w1, w2, . . . , w5 are various tunable param-

eters. Figure A.5 shows the dataflow for the onset detection algorithm.

Audio in Frame Window FFT Mag

Filter Fiux Onsets

Figure A.5: Sequential dataflow for Spectral Onsets

Name Description Default Value

sampleRate Target (expected) sample rate of audio inputs 44100frameSize Frame size (number of samples) for the framing

function1024

stepSize Step size (number of samples) for the framingfunction

512

windowType Windowing function (bartlett, blackman, black-manHarris, hamming, hann)

hann

ratio Minimum activation ratio for windowing func-tion

0.22

threshold Minimum threshold (δ) for peak-picking 2.5

Table A.5: Spectral Onset Parameters

46

Appendix B

Evaluation Dataset

47

Tab

leB

.1:

Fu

llE

valu

ati

on

Data

set

Song

Artist

Album

Genre

MD5

Han

gin

gT

ree

Cou

nti

ng

Cro

ws

Satu

rday

Nig

hts

&..

.A

lter

nati

ve7882ab

09d

5a2330e8

6d

184410d

985d

b1

Dra

wM

eD

own

Pat

Mar

tin

oM

issi

on

Acc

om

pli

shed

Jazz

c7a198ff

c99ea

05166fe

6d

d11fe

db

461

Su

mm

erti

me

Rol

ls(

...

Jan

e’s

Ad

dic

tion

Noth

ing’s

Sh

ock

ing

Alt

ern

ati

ve35d

132b

0d

7fb

f24946fc

1d

f98f9

f437d

(For

ewor

d)

Pai

nO

fS

alva

tion

Entr

op

iaP

rog.

Met

al

a2eb

384

6951687377255b

6f1

27f9

d1b

b

2000

Lig

ht

Yea

rsA

way

Gre

enD

ayK

erp

lun

k!

Pu

nk

74523401139759d

223ad

3007f3

545b

59

3W

eeks

InL

aym

enT

erm

s3

Wee

ks

InIn

die

921855a0f3

f5d

36afd

92b

dc3

7741b

943

3rd

Pla

net

Mod

est

Mou

seT

he

Moon

&A

nta

rc..

.In

die

560c9

73b

19c1

2e2

67f5

3d

4b

544c1

73e4

Hol

iday

sJoh

nS

cofi

eld

Ou

tL

ike

AL

ight

Jazz

1819b

9f7c3

8695823fe

dee

c4151ed

3b

a

AF

eed

bag

ofT

ruck

s...

Lag

wag

onL

et’s

talk

ab

ou

t..

.P

un

kd

53f2

e3c8

fa924b

f840cd

40601a1a3a8

Ace

sH

igh

Iron

Mai

den

Pow

ersl

ave

Met

al

05018d

11e5

0f6

0f8

8861cff

9b

b5c2

cc9

Ad

dV

ice

Lov

eM

eD

estr

oyer

Bla

ckH

eart

Aff

air

Pu

nk

6d

b19b

64b

d9d

e5f9

1c4

1ec

c2a70b

28b

1

Ad

vent

Op

eth

Morn

ingri

seP

rog.

Met

al

c945b

0880f4

aef

e97a2d

7b

49d

34ca

6ac

Aft

erY

ouM

yF

rien

dL

agw

agon

Let

’sT

alk

Ab

ou

t..

.P

un

k5ca

a8e2

d3ef

29814fb

e0e7

cb7c7

37396

All

At

Sea

(Liv

eat

...

Jam

ieC

ull

um

Catc

hin

gT

ale

sJazz

75e3

ba8

b0b

c892d

6b

443f1

63c5

7b

6a33

All

For

You

Ou

rL

ady

Pea

ceG

ravit

yA

lter

nati

vef7

9d

e432e5

01c4

cf5b

a7595fd

42d

2733

An

gels

Hol

oca

ust

Iced

Ear

thN

ight

Of

Th

eS

tor.

..M

etal

2ec

8aca

5025f3

5cb

dd

ae8

d2e8

77d

0a8f

An

gels

Los

ing

Sle

epO

ur

Lad

yP

eace

Hea

lthy

InP

ara

no..

.A

lter

nati

ve35e3

20a0f7

2b

c40671f5

0e1

fb2d

d2254

An

imae

Par

tus

(’I

Am

’)P

ain

Of

Sal

vati

on

Be

Pro

g.

Met

al

ab

ff5389

ac9

6129ef

49cf

7c9

4d

0a34cb

Bab

elM

um

ford

&S

ons

Bab

elF

olk

Rock

3e2

bb

42925550b

0927cb

8231f0

dca

352

48

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Bag

ItU

pO

asis

Dig

Ou

tY

ou

rS

oul

Alt

ern

ati

vecb

ee488

a1963b

ac4

8ef

d3248b

85ae3

d4

Beg

inn

ings

Pai

nO

fS

alva

tion

Rem

edy

Lan

eP

rog.

Met

al

66a883b

72d

781714139fb

00b

15cc

717c

Bid

in’

My

Tim

eJoe

Pas

sIr

a,

Geo

rge

An

dJoe

Jazz

b423d

a7c4

c76fe

9834830b

7ab

6d

7689b

Bla

ckD

ogL

edZ

epp

elin

Led

Zep

pel

inIV

Rock

cfe7

2b

189d

267cb

3d

f70f4

e246f9

2ec

6

Bli

zzar

dO

f’7

7N

ada

Su

rfL

etG

oIn

die

d576420

f021075a870c6

95fc

1fc

32d

db

Bri

ckw

ork,

Par

t1

-...

Pai

nO

fS

alva

tion

12-5

Pro

g.

Met

al

2ed

9242

23217cc

63794e0

44b

bcc

f47a5

Bu

llio

nM

ille

nco

lin

Lif

eon

aP

late

Pu

nk

c6f5

6d

5fd

81d

255c7

1e4

cfd

317c7

6a3d

Bu

rnL

agw

agon

Bla

zeP

un

k3b

4e4

adcf

e0a755d

6d

32b

cc00044ac1

7

Bu

rnou

tG

reen

Day

Dookie

Pu

nk

3482230b

f80309c2

60b

fd2285708222e

Cau

ght

Som

ewh

ere

in..

.Ir

onM

aid

enS

om

ewh

ere

inT

ime

Met

al

6139ee

48b

d373a4161a614c7

7b

2b

402e

Ch

oked

An

dC

har

med

Lov

eM

eD

estr

oyer

Th

eT

hin

gs

Aro

un

d..

.P

un

k81d

1692

5f6

6c1

7d

962f7

3ef

6b

bef

dd

94

Cit

yB

oyB

lues

Mtl

eyC

reT

hea

tre

Of

Pain

Met

al

2a6e6

5c7

2f1

95ce

e17f5

dcc

7a51e8

6c8

Con

cret

eB

edN

ada

Su

rfT

he

Wei

ght

isa

Gif

tIn

die

0992d

43f5

050d

929e3

b4b

697795ab

223

Cow

boy

sF

rom

Hel

lP

ante

raC

owb

oys

Fro

mH

ell

Met

al

82f5

ebcd

e5ee

9b

77d

f953cc

73828cf

e9

Cra

zyT

rain

Ozz

yO

sbou

rne

Th

eE

ssen

tial

Ozz

...

Rock

483d

7c8

b54313a2332d

933ab

24a0694e

Cro

pC

ircl

eM

onst

erM

agn

etP

ower

Tri

pS

ton

erM

etal

b049538

04746160cc

89f6

28d

095e5

326

Cry

Tou

ghP

oiso

nL

ook

Wh

at

Th

eC

at.

..M

etal

2f8

74d

b33cf

cbc2

0469f7

4c1

372c2

247

Cycl

ops

Rev

olu

tion

Mon

ster

Mag

net

Su

per

jud

ge

Sto

ner

Met

al

fc60f8

e77d

ebf9

c7ca

fb43e1

33b

17b

67

D’Y

ouK

now

Wh

atI

M..

.O

asis

Be

Her

eN

owA

lter

nati

ve3aaf9

ab

947c5

58154537cb

5c4

8f5

2b

7b

Dan

ny’s

Son

g(L

oggi

...

Me

Fir

stA

nd

Th

eG

i...

Hav

eA

Ball

Pu

nk

31f6

93d

ad

d240d

890b

3461e8

09d

08ce

b

49

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Dar

kS

aga

Iced

Ear

thT

he

Dark

Saga

Met

al

b5ea

1ee

fb2aee

0e4

74d

fa91b

b79acf

63

Day

Job

Gin

Blo

ssom

sC

on

gra

tula

tion

sI.

..In

die

9d

cae0

9a35fd

efb

f4ab

30d

e0a57ef

bad

Des

ert

Skie

sL

aym

enT

erm

sD

rive

To

Now

her

e-..

.In

die

181a33d

a8a378a78042e4

8fe

c65a4a57

Dis

app

ear

Maz

zyS

tar

Am

on

gM

yS

wan

Alt

ern

ati

ve50901c3

4a28584fd

1c2

7114345cf

69c8

Doi

ng

ItF

orT

he

Kid

sN

obod

ys

Gen

erati

on

XX

XP

un

k4b

f4a35

c41a8ed

21b

72c1

d65e0

7d

e445

Dop

esT

oIn

fin

ity

Mon

ster

Mag

net

Dop

esT

oIn

fin

ity

Sto

ner

Met

al

2590f9

b0c6

ea1a7fd

cb88f9

2338d

55e8

Fad

eIn

toY

ouM

azzy

Sta

rS

oT

on

ight

Th

at

I...

Alt

ern

ati

veafc

6c7

db

005538a9a305a2b

2a34e2

b21

feve

rM

ich

ael

Bu

ble

Wit

hL

ove

Jazz

e15c2

f7610f1

575336e6

27c3

94f0

db

c4

Fol

low

Th

rou

gh(S

tr..

.G

avin

DeG

raw

Ch

ari

ot

-S

trip

ped

Pop

Rock

0c8

acb

8cb

883b

730fd

a713580a670a94

Fol

low

Th

rou

ghG

avin

DeG

raw

Ch

ari

ot

Pop

Rock

ac7

45e9

d798d

527aa9f7

8882b

886cc

e2

For

ce-F

edG

od

For

bid

Gon

eF

ore

ver

Met

al

27334a7ec

b14f3

5c1

27ec

cfcc

9c6

078e

For

get

Her

Jeff

Bu

ckle

yG

race

Dis

c2

Ind

iec4

e429fa

ebeb

ae6

feb

166190d

c0a6b

90

Fro

mT

he

Roof

top

Dow

nN

ada

Su

rfT

he

Wei

ght

IsA

G..

.In

die

a56ed

273d

62e7

dc5

377a30cb

97d

32f7

0

F**

*in

’In

Th

eB

ush

esO

asis

Sta

nd

ing

On

Th

eS

...

Alt

ern

ati

vec3

460c0

0ed

084b

d6686b

99b

e81111584

Fu

ture

sJim

my

Eat

Worl

dF

utu

res

Ind

ie0226a02155098c1

8d

2e7

4fd

327cc

4cb

3

Gas

olin

eK

isse

sF

or..

.P

ark

ItW

on

’tS

now

Wh

e...

Pu

nk

c419cc

df4

75fd

79a12ce

3ac4

10f2

0a49

Giv

em

eth

eN

ight

Geo

rge

Ben

son

Ver

yb

est

of

-G

r...

Jazz

48b

9935

c31d

4c1

4398517a4064a5e7

8a

Go

Pea

rlJam

Vs.

Alt

ern

ati

ve6089d

775728ed

129690b

56109a4b

bee

1

God

Kn

ows

IL

ove

Yo.

..L

oren

eD

rive

Rom

anti

cW

ealt

hP

un

k17b

7502

9b

08b

0f6

378fc

22c9

ad

07b

27b

Gon

na

See

My

Fri

end

Pea

rlJam

Back

space

rA

lter

nati

ve109ce

76eb

f124f8

d0ee

e277afd

dd

ab

e2

50

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Good

Tim

esB

adT

imes

Led

Zep

pel

inL

edZ

epp

elin

Rock

03d

1519

d4b

58180e3

00fd

247a49c2

46d

Intr

oG

oril

laz

Dem

on

Day

sH

ip-H

op

cf197cd

ef57e0

b8ce

0ca

7aef

5ab

7e3

a9

Hea

rtis

Har

dto

Fin

dJim

my

Eat

Worl

dIn

vente

dIn

die

a23d

7f7

65afa

6ad

1b

dd

7e0

91b

dd

9b

345

Hea

rtb

reak

ing

Mu

sic

Lag

wag

onR

esolv

eP

un

k4f7

e11833e3

b39e3

de0

db

0cb

ce78c4

cf

Hel

loO

asis

(Wh

at’

sT

he

Sto

ry..

.A

lter

nati

ve7391d

6e0413f9

a5c4

149b

41ec

dd

0754b

Hol

yF

***

IA

mT

he

Ava

lan

che

Ava

lan

che

Un

ited

Pu

nk

7986ee

f40b

4c4

20e9

5a53e1

d277f9

268

Hol

yW

ars.

..T

he

Pu

...

Meg

adet

hR

ust

InP

eace

Met

al

7515b

288ed

22a3302458e3

00b

ad

c49ca

How

We

Get

Alo

ng

Ju

rass

ic5

Qu

ali

tyC

ontr

ol

Hip

-Hop

2a9b

bb

93f3

edad

1cd

c288e6

5781c9

075

ID

on’t

Kn

owO

zzy

Osb

ourn

eB

lizz

ard

of

Ozz

Rock

631f5

1c7

5e8

78043ea

8ce

42786d

da81e

Imm

igra

nt

Son

gL

edZ

epp

elin

Led

Zep

pel

inII

IR

ock

0b

ce4fa

bc4

7aa3b

e3a40d

c2aef

61f8

12

InT

he

Eve

nin

gL

edZ

epp

elin

InT

hro

ugh

Th

eO

u..

.R

ock

d62e0

c9c0

6c3

5849807ea

480926b

126c

InT

he

Mea

nti

me

Hel

met

Mea

nti

me

Alt

ern

ati

ve7fd

e7ea

084ec

9b

21ce

89cc

6769cb

901f

Ind

epen

den

ceD

ayN

oM

otiv

Day

light

Bre

akin

gP

un

ke0

8d

820

6c3

c54e7

3f6

dad

a8031d

f1a9d

Into

You

rE

yes

Lu

cero

Th

eA

ttic

Tap

esA

lt-C

ou

ntr

y1769f3

f2ca

9cd

c155cb

ec363d

1e5

4b

64

Intr

od

uct

ion

Pan

ic!

At

Th

eD

isco

AF

ever

You

Can

’t...

Pu

nk

f9680585c8

f690468f3

07e9

dd

977452d

Inva

der

sIr

onM

aid

enT

he

Nu

mb

erO

fT

he.

..M

etal

5395cc

d950fb

6598b

76684b

0cd

7a91f1

Wh

atA

Diff

eren

ceA

...

Jam

ieC

ull

um

Tw

enty

som

eth

ing

Jazz

b73afa

bf4

38c6

6fc

957905471c2

0f4

46

Jaw

sof

Lif

eJoh

nP

etru

cci

Su

spen

ded

An

imati

on

Pro

g.

Met

al

a1d

a6fc

b4622c6

7a22f3

77ef

2ee

77f0

4

Ju

stO

ne

Of

Th

ose

T..

.Jam

ieC

ull

um

Th

eP

urs

uit

Jazz

81b

e1cf

ab

53c8

6b

d37025f6

7950967af

Ju

stifi

edB

lack

Eye

No

Use

For

AN

am

eL

ech

eC

on

Carn

eP

un

k268f9

0c6

9ec

7d

d57ce

71b

5665b

3ea

ee2

51

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Kid

sD

on’t

Lik

eT

o..

.L

agw

agon

Hoss

Pu

nk

c4d

9d

0425d

9331b

2b

c471e7

fd32715d

4

Las

tof

the

Anti

-Fa.

..K

ane

Hod

der

Th

eP

leasu

reto

R..

.P

un

kf7

fc3f6

4ef

2b

7a60e4

ba61293ec

5e9

81

Lif

eon

aC

hai

nP

ete

Yor

nm

usi

cfort

hem

orn

in..

.A

lter

nati

ve989580a25ef

66c9

b52ea

6cc

d57d

25793

Lif

eW

aste

dP

earl

Jam

Pea

rlJam

Alt

ern

ati

veb

29e9

79b

a2b

aa9d

95f3

f0d

343c7

0f3

9d

Lin

oleu

mN

OF

XP

un

kIn

Dru

bli

cP

un

k5b

d99e5

956d

ca06c7

14b

3a040d

678d

80

Lit

tle

Dip

per

Hu

mY

ou

’dP

refe

ran

A...

Alt

ern

ati

ve16538e3

b8d

2b

49645e8

7e4

80b

62b

35f5

Liv

ing

Aft

erM

idn

ight

Ju

das

Pri

est

Bre

akin

gth

eL

awM

etal

14ce

49d

bd

5b

a191f7

112e7

cc67a9f4

54

Los

tH

oriz

ons

Gin

Blo

ssom

sN

ewM

iser

ab

leE

xp

...

Ind

ie1c2

b4ee

7c5

a65334e1

7d

0265b

251a2ce

Mam

a,I’

mC

omin

gH

ome

Ozz

yO

sbou

rne

Th

eE

ssen

tial

Ozz

...

Rock

d604c7

97ae6

9ab

1fc

cf6128d

a2e0

e425

Mat

tS

kib

a-

Good

F..

.M

att

Skib

aS

pli

tIn

die

da9145f

936209f0

b2ed

6c1

0649e8

845e

Mel

tM

onst

erM

agn

etG

od

Say

sN

oS

ton

erM

etal

8c8

e99fc

ec6949fe

71a5ab

2d

fd23ff

1a

Mo

joP

inJeff

Bu

ckle

yG

race

Dis

c1

Ind

ie694848469f3

fcb

83a0fe

ed9d

51f4

b6cf

Mou

thF

orW

arP

ante

raV

ulg

ar

Dis

pla

yof.

..M

etal

f4c2

dd

7063cd

e0e7

ac6

617a2cb

dac7

19

Ele

ctro

cuti

onN

ada

Su

rfIf

IH

ad

aH

i-F

iIn

die

a8af0

64b

061132493103001934b

dd

cdf

New

Dir

ecti

onG

oril

laB

iscu

its

Sta

rtT

od

ayP

un

kaf1

4b

030b

1a5e9

325542e8

0a94d

a7a27

Nig

htm

ares

by

the

Sea

Jeff

Bu

ckle

yS

ketc

hes

for

My

S..

.In

die

db

3f2

e02f1

6d

2187549d

dd

b6d

9ac0

675

Div

eN

irva

na

Ince

stic

ide

Alt

ern

ati

ved

57e3

f21444445f8

a6d

537295d

13679f

Ser

veT

he

Ser

vants

Nir

van

aIn

Ute

roA

lter

nati

ve77ea

1b

418d

3594423988ad

9174762d

37

Sm

ells

Lik

eT

een

Sp

...

Nir

van

aN

ever

min

dA

lter

nati

ve2d

5c0

46e6

6ce

e3b

f4b

aef

48ad

9d

2c3

d7

No

Cig

arM

ille

nco

lin

Pen

nyb

rid

ge

Pio

nee

rsP

un

k8472f6

ad

2e9

5cd

19e7

bc7

2f9

4b

4760b

7

52

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Acq

uie

sce

Oas

isT

he

Mast

erp

lan

Alt

ern

ati

vef5

2ff

6c7

f62996ff

0c0

784fa

b881a678

On

ceP

earl

Jam

Ten

(Del

uxe

Ed

itio

n)

Alt

ern

ati

ve0638cf

b17485b

b98874a76b

bb

ec1b

b40

On

eM

anA

rmy

Ou

rL

ady

Pea

ceH

ap

pin

ess.

..Is

N..

.A

lter

nati

ve59f9

bf2

708af9

d6d

7656ef

be9

25245b

9

On

eQ

uie

tN

ight

Pat

Met

hen

yO

ne

Qu

iet

Nig

ht

Jazz

049d

0a2

05f5

a6f9

a7f9

947107915cd

33

Orc

hes

tral

Intr

o(f

...

Gor

illa

zP

last

icB

each

Hip

-Hop

9fd

734c

1a4ef

52016c3

70560ee

cd03d

f

Ove

rT

he

Rai

nb

ow(W

...

Me

Fir

stA

nd

Th

eG

i...

Are

AD

rag

Pu

nk

93c2

c56e0

e0663ad

caab

8a61cb

12b

077

Pau

seK

idD

yn

amit

eK

idD

yn

am

ite

Pu

nk

16541e8

7688202b

dcc

255ab

38a10b

a92

Pea

cefu

lD

ayP

ennyw

ise

Ab

ou

tT

ime

Pu

nk

4d

6aee

60436141b

a1ea

d7d

28f1

f9a8c7

Bre

aker

fall

Pea

rlJam

Bin

au

ral

Alt

ern

ati

ve5d

6732b

76d

5d

81e1

c940355b

e1c9

d213

Can

’tK

eep

Pea

rlJam

Rio

tA

ctA

lter

nati

ved

b1cc

e4b

f7e3

2571b

ea3318cf

9098ed

c

Las

tE

xit

Pea

rlJam

Vit

alo

gy

Alt

ern

ati

ve3a6ea

d2d

99d

a072b

de6

96c5

2f3

f5f5

65

Pil

lS

hov

elM

onst

erM

agn

etS

pin

eof

God

Sto

ner

Met

al

173c4

2e7

3a3cf

1063618d

e7541b

db

606

Pit

sA

nd

Poi

son

edA

...

Kid

Dyn

amit

eS

hort

er,F

ast

er,L

o..

.P

un

ke9

52c3

f26ab

0cf

12d

1a6d

c5a4212b

837

Pu

reH

elm

etA

fter

tast

eA

lter

nati

ve9d

90ab

d1c6

4347c5

988a75458ad

fba77

R.K

.In

tro

Ou

rL

ady

Pea

ceS

pir

itu

al

Mach

ines

Alt

ern

ati

vead

3d

70a679991e3

8ad

93e0

560ec

b6486

Re-

Has

hG

oril

laz

Gori

llaz

Hip

-Hop

3d

79f7

f00e1

04798cb

f8685c5

af3

a827

Rem

ind

erG

rey

Are

aF

anb

elt

Alg

ebra

Pu

nk

d1a4aee

1b

6994a13049881716c1

ceaab

Rep

etit

ion

Hel

met

Str

ap

ItO

nA

lter

nati

ved

2d

f5b

029c5

52c7

27b

63a1081c6

76893

Rig

ht

Now

!G

rey

Are

aG

reya

rea

Pu

nk

af3

0aea

5b

f868a9ef

00f9

c8d

89a16d

f6

Rip

ItO

ut

Kis

sA

ceF

reh

ley

Rock

c2a011ee

7360b

01ef

112f9

414e4

d71cd

53

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Rock

’N’

Rol

lS

tar

Oas

isD

efin

itel

yM

ayb

eA

lter

nati

veef

874b

edfb

5f2

b73ef

06005681f3

1b

d6

Ru

by

Kai

ser

Ch

iefs

You

rsT

ruly

,A

ngr.

..In

die

a996a7415b

6d

e75a3e2

14a9640a7776c

Ru

mb

leY

oun

gM

anR

u..

.P

inh

ead

Cir

cus

Th

eB

lack

Pow

ero..

.P

un

kc5

0cd

aacb

b5cf

9a5a11e7

eab

915b

ac2

8

Sal

tS

wea

tS

uga

rJim

my

Eat

Worl

dB

leed

Am

eric

an

Ind

ie44e3

dcc

41606c0

f24b

a8d

2b

be8

e7a0b

8

Sat

elli

teS

kin

Mod

est

Mou

seN

oO

ne’

sF

irst

,A

...

Ind

ieaf7

0514b

f6616337cf

cfe2

d06cf

dd

6d

4

Sca

rsic

kP

ain

Of

Sal

vati

on

Sca

rsic

kP

rog.

Met

al

8b

df1

4b

7c1

aa06c5

ccd

a29d

bb

e36b

e2a

Scr

eam

ing

For

Ven

ge..

.Ju

das

Pri

est

Met

al

Work

s’7

3’.

..M

etal

606191afc

0ad

9aa19e2

1d

784236ca

3a9

See

Th

eW

ind

Pol

arB

ear

Clu

bC

hasi

ng

Ham

bu

rgP

un

k4b

86cb

11b

1a072c8

0d

3a4e9

fa4c8

85e2

Sh

adow

sO

fD

efea

tG

ood

Rid

dan

ceO

per

ati

on

Ph

oen

ixP

un

k8836205c1

ff8b

b8b

6afd

feeb

1f3

e1aa5

Sh

amb

les

Mak

eD

oA

nd

Men

dB

od

ies

Of

Wate

rP

un

k95e0

94e0

f6ad

eab

a164c9

7c5

ec9d

2e0

5

Sig

hN

oM

ore

Mu

mfo

rd&

Sons

Sig

hN

oM

ore

Folk

Rock

c05ce

9f5

29280072b

89f9

4a9c8

8d

98b

0

Skin

O’

My

Tee

thM

egad

eth

Cou

ntd

own

To

Exti

...

Met

al

c7b

ea38

6a7c9

c40412a7a376202b

9614

Slu

tM

ach

ine

Mon

ster

Mag

net

Mon

oli

thic

Baby!

Sto

ner

Met

al

6757d

7ca7c4

3ae8

6c6

427153527b

db

fa

Sm

art

Hel

met

Siz

eM

att

ers

Alt

ern

ati

ve95866e0

d750e5

71710b

086f5

29d

ffcb

8

Sm

oke

Lu

cero

1372

Over

ton

Park

Alt

-Cou

ntr

y918f5

2f8

deb

2963111e8

d79174f8

bf8

3

Sn

ake

Dan

ceM

onst

erM

agn

etM

on

ster

Magn

etS

ton

erM

etal

f939675835166102fa

fb507ff

da2d

f02

So

Lon

gH

elm

etS

eein

gE

ye

Dog

Alt

ern

ati

vea84d

6c9

540c0

26b

5d

718fc

f365f0

ad

4f

So

Wh

atG

eorg

eB

enso

nB

eyon

dT

he

Blu

eH

...

Jazz

18a438b

d509ce

76ae0

4d

4d

952822584e

Som

etim

esP

earl

Jam

No

Cod

eA

lter

nati

ved

cfec

22ef

117528e6

81271722380810c

Sp

eak

To

Me

/B

reat

he

Pin

kF

loyd

Dark

Sid

eof

the

...

Rock

37b

d9e9

2c0

006f3

db

005d

dc1

d8f9

e94a

54

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Sp

irit

ofth

eL

and

Pai

nof

Sal

vati

on

On

eH

ou

rby

the

C..

.P

rog.

Met

al

634ae3

fd140915d

231b

f688e7

d146ec

6

Su

per

man

’sD

ead

Ou

rL

ady

Pea

ceC

lum

syA

lter

nati

vea21aa4c5

a2b

daf4

d5443e6

d20c7

660c8

Sw

allo

win

gE

very

thin

gH

elm

etM

on

och

rom

eA

lter

nati

ve35f3

a1564481b

68554a88c8

8b

c7ff

dc9

Sw

eet

Lit

tle

Th

ing

Lu

cero

Ten

nes

see

Alt

-Cou

ntr

ycc

0d

251

979b

f311cc

2b

f9c9

082c9

9243

Sw

eet

Hom

eA

lab

ama

Lynyrd

Skynyrd

Gold

(Rem

ast

ered

)R

ock

70c2

eb2b

4c9

4a33575c6

e1e9

10635f9

6

T.n

.T.

(Ter

ror

’nT

...

Mtl

eyC

reD

r.F

eelg

ood

Met

al

745773416fb

da3fb

04f7

f11d

480b313e

Tab

...

Mon

ster

Mag

net

Tab

...2

5S

ton

erM

etal

cf036f5

acc

3012f1

06ce

cc14354d

5721

Tab

leF

orG

lass

esJim

my

Eat

Worl

dC

lari

tyIn

die

6c3

84a9b

9063406cd

8f4

710f1

8f2

03a3

Th

anks

for

the

Kil

l...

Min

us

the

Bea

rH

igh

lyR

efin

edP

i...

Ind

ie1b

11753

1a7f3

15b

7046ef

be0

6d

731f2

6

Th

atM

uch

Fu

rth

erW

...

Lu

cero

Th

at

Mu

chF

urt

her

...

Alt

-Cou

ntr

yd

0ea

886

8fc

d2e9

334f9

ffc5

4a9ed

385c

Th

atM

uch

Fu

rth

erW

est

Lu

cero

Th

at

Mu

chF

urt

her

...

Alt

-Cou

ntr

yd

8d

2b

53e9

b95b

84fa

75d

44fd

d4891ab

9

Th

eB

ird

man

Ou

rL

ady

Pea

ceN

avee

dA

lter

nati

ve7312438a18174ed

944c3

c37c1

02af5

ea

Th

eB

oat

Dre

ams

fro.

..Jaw

bre

aker

24

Hou

rR

even

ge

T..

.P

un

kd

c9b

5e1

fb5fd

46636aa4f5

57b

507d

764

Th

eF

row

nin

gof

aL

...

Hey

Mer

ced

esE

very

nig

ht

Fir

eW

...

Ind

ie17a7d

afb

fa07ad

d20765d

ed7f3

aaad

aa

Th

eG

ame

Nee

ded

Me

Min

us

Th

eB

ear

Men

os

El

Oso

Ind

ie668aa697d

883d

30b

6b

7fd

dcb

5b

486a72

Th

eG

hos

tY

ouA

reP

ark

No

Sig

nal

Pu

nk

81413b

f9577090d

1a5c3

bd

7c3

bad

e10e

Th

eH

elli

onJu

das

Pri

est

Met

al

Work

s’7

3-’

...

Met

al

73f0

9a25376d

e132cf

0ab

387549f3

e38

Th

eH

ind

uT

imes

Oas

isH

eath

enC

hem

istr

yA

lter

nati

vea08333248b

ebb

54ad

28d

7043ff

5fd

bd

2

Th

eS

ky

Isa

Lan

dfi

llJeff

Bu

ckle

yS

ketc

hes

for

My

S..

.In

die

cca31e7

58e4

cffe3

c9d

aa1d

1b

dc1

2e3

2

Th

eS

ong

Rem

ain

sT

h..

.L

edZ

epp

elin

Hou

ses

Of

Th

eH

oly

Rock

56b

5839

857d

02d

c8fe

22fa

9fb

4004fa

2

55

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Tip

You

rB

arte

nd

erG

lass

jaw

Wors

hip

&T

rib

ute

Pu

nk

00474572a89c7

28d

9c1

fb9344b

afc

00f

Tru

eN

atu

reJan

e’s

Ad

dic

tion

Str

ays

Alt

ern

ati

ve2650462909d

c31cb

ad

5d

9e2

a5d

a4566a

Tru

stM

egad

eth

Cry

pti

cW

riti

ngs

Met

al

67c6

a883f0

fc2c4

771c8

a2ef

c4b

25e8

f

Un

kn

owin

gly

Str

ong

Mak

eD

oA

nd

Men

dE

nd

Mea

sure

dM

ile

Pu

nk

fbad

2d

86b

b7ec

210d

520d

bf0

d82ad

506

Use

dP

ain

Of

Sal

vati

on

Th

eP

erfe

ctE

lem

e...

Pro

g.

Met

al

4608d

c0f9

ff4a0359e6

a291d

0d

5ea

e36

Wal

kD

on’t

Ru

nJoh

nny

Sm

ith

Kale

idosc

op

eJazz

ee6730a7d

6d

2e3

315589e3

a78e3

ec49c

Wav

esG

uth

rie

Gov

an

Ero

tic

Cakes

Pro

g.

Met

al

d4fc

92c

c644305484d

3116c6

fd9e9

07c

Wh

at’s

You

rN

ame

Lynyrd

Skynyrd

Str

eet

Su

rviv

ors

Rock

ecc9

9ac0

1b

7506f8

23c0

22c8

3fe

dc9

ee

Wh

ats

You

rN

ame

Lynyrd

Skynyrd

Gold

(Rem

ast

ered

)R

ock

23082a884b

0fd

85d

ea6b

e8f8

cd201789

Wh

ere

Eag

les

Dar

eIr

onM

aid

enP

iece

Of

Min

dM

etal

690461920c7

fae0

d140aff

451ce

778d

8

Wh

ole

Lot

taL

ove

Led

Zep

pel

inL

edZ

epp

elin

IIR

ock

2e5

e7fc

2c8

e3d

ead

04d

bf0

fda994566e

Wil

dS

ide

Mtl

eyC

reG

irls

,G

irls

,G

irls

Met

al

b936570

b3106a5b

6a7b

eba75711992a4

You

Kn

owY

ou’r

eR

ight

Nir

van

aN

irva

na

Alt

ern

ati

ve5746991922a151ee

29fd

b48c0

8e3

ec9a

You

Mov

eM

eH

ello

Th

eG

lam

Sin

gle

s..

.R

ock

6b

e0785

8918d

b4123d

73a0612d

caa916

Bel

lsH

eyM

erce

des

Hey

Mer

ced

esE

PIn

die

c1355228219212ae1

4e1

d6001c1

99ee

a

Dea

dA

nd

Gon

eI

Am

Th

eA

vala

nch

eI

Am

Th

eA

vala

nch

eP

un

k4b

7d

efc4

9d

437f7

b47b

08cb

9311f9

bd

8

Dra

mam

ine

Mod

est

Mou

seT

his

IsA

Lon

gD

r...

Ind

ie6b

df2

40cb

978870ce

2ce

8aec

36d

06c1

c

Au

tom

atic

Syst

emat

i...

Gar

bag

eN

ot

You

rK

ind

Of

...

Alt

ern

ati

ve4fc

346ed

c7b

2993931c3

810f2

1973ec

5

Rou

lett

eS

yst

ems

Hey

Mer

ced

esU

norc

hes

trate

dE

PIn

die

6ec

f141b

da642203cc

0169026d

f3ad

9c

Lit

tle

Sil

ver

Hea

rtL

uce

roL

uce

roA

lt-C

ou

ntr

ya277665c7

c32f9

f2459554c6

af6

1043a

56

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Mar

chin

toth

eS

eaM

od

est

Mou

seW

eW

ere

Dea

dB

efo..

.In

die

936c4

45a20b

5b

2e7

8657455ae5

77e5

fa

Hal

ahM

azzy

Sta

rS

he

Han

gs

Bri

ghtl

yA

lter

nati

vec7

2c6

6036d

6f4

03b

efa6528ce

38628d

2

Fee

lin

gG

ood

Mic

hae

lB

ub

leIt

’sT

ime

Jazz

b819d

22452066ff

255d

ad

48074110d

60

Nev

erE

ndin

gM

ath

E..

.M

od

est

Mou

seB

uil

din

gN

oth

ing

...

Ind

iea8ea

5b

cbb

7cb

8687d

57b

ae2

ad

01f6

e1d

Dir

tyP

aws

Of

Mon

ster

san

dM

enM

yH

ead

Isan

An

imal

Ind

ie83961d

d90b

5b

9fe

bff

caea

786425d

001

Old

Dev

ilM

oon

Jam

ieC

ull

um

Hea

rdIt

All

Bef

ore

Jazz

ee18120ee

e9837b

78525b

d729f2

aeb

7f

Ph

oner

To

Ari

zon

aG

oril

laz

Th

eF

all

Hip

-Hop

2382ce

1e1

b4b

434aee

7881b

dd

b322384

Qu

alit

yR

even

geA

t..

.H

eyM

erce

des

Lose

sC

ontr

ol

Ind

ie699f0

3d

b7954678a83b

ca97623f7

02ee

See

Th

ese

Bon

esN

ada

Su

rfL

uck

yIn

die

ad

b2d

841a96f3

099b

51a67d

81d

5d

e58d

Su

nsp

ots

Mod

est

Mou

seT

he

Fru

itT

hat

At.

..In

die

584c4

55138ee

c097c3

58cf

91b

cca2ef

a

Tee

thL

ike

God

’sS

h..

.M

od

est

Mou

seT

he

Lon

esom

eC

row

...

Ind

ie329a5d

6a45039092f6

62c4

ebf7

b1cc

e9

Th

eF

ire,

Th

eS

teel

...

Hot

Wat

erM

usi

c”T

he

Fir

e,T

he

St.

..P

un

kb

dac7

e9f5

fb9204c4

5264591f5

dd

6615

Ton

ight

Ain

’tG

onn

a...

Lu

cero

Dre

am

ing

InA

mer

ica

Alt

-Cou

ntr

y8d

d8e1

456d

5fc

1803a494f7

297d

5fb

b1

Tu

rnU

pT

he

Su

nO

asis

Don

’tB

elie

ve

Th

e...

Alt

ern

ati

ve54f9

77b

fe610ec

2b

1e7

519aea

7183fd

1

Wat

chIt

Bu

rnL

uce

roN

ob

od

y’s

Darl

ings

Alt

-Cou

ntr

y2aa45680e5

f138c1

e5d

acc

c9a9931ca

e

Wh

atE

lse

Wou

ldY

ou..

.L

uce

roR

ebel

s,R

ogu

es&

...

Alt

-Cou

ntr

yac1

a6a9f0

09151d

6512496336539ec

3c

Wil

lfu

lS

usp

ensi

on..

.M

od

est

Mou

seE

very

wh

ere

An

dH

i...

Ind

ied

5ab

9f0

993b

cfe9

b08f4

56d

b7d

205d3b

Wor

ms

vs.

Bir

ds

Mod

est

Mou

seS

ad

Sap

py

Su

cker

Ind

ied

4d

dcd

1622019750148067341fd

90c3

a

220

Yea

rsH

otW

ater

Mu

sic

Fu

elfo

rth

eH

ate

...

Pu

nk

a9c7

aac2

9f2

f4e4

bb

cde5

5ad

12d

906b

f

AF

ligh

tan

da

Cra

shH

otW

ater

Mu

sic

AF

light

an

da

Cra

shP

un

ka575c1

f1e0

8406178653418b

a2f8

6c9

8

57

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

AF

oxxy

Chic

kJoe

Pas

sE

xim

iou

sJazz

39a89330b

0fd

5d

5e9

0c5

37cb

6b

e9e4

6e

AM

apof

the

Wor

ldP

atM

eth

eny

AM

ap

of

the

Worl

dJazz

fb43fb

7863599b

29475971aeb

71cd

55e

AS

tree

tcar

Nam

edD

...

Ken

ny

Bu

rrel

l’R

ou

nd

Mid

nig

ht

Jazz

547ec

9d

449082749862141273aca

7ea

2

Aco

ust

icB

lues

Joe

Pas

sV

irtu

oso

No

4D

isc

2Jazz

c712512b

ec6b

53d

709b

72c2

3a38ee

0c2

Ala

chu

aH

otW

ater

Mu

sic

Nev

erE

nd

erP

un

k373cf

5d

7d

c530b

d2e4

f5e2

584c4

b0826

All

You

Did

Was

Sav

...

Ou

rL

ady

Pea

ceB

urn

Bu

rn(D

elu

xe.

..A

lter

nati

ve4188772d

888675e4

def

57313a180801c

An

gel

Wit

hT

he

Blu

esK

evin

Eu

ban

ks

Pro

mis

eO

fT

om

orr

owJazz

636305b

470e2

0b

fe1b

79fd

e0fd

7c9

c1b

Big

Cas

ino

Jim

my

Eat

Worl

dC

hase

Th

isL

ight

Ind

iec7

5d

276

8cb

2049cc

b9ec

821a78ae7

d09

Bit

ters

wee

tJim

my

Bru

no

Con

cord

Jazz

Gu

it..

.Jazz

987694d

6192f4

bec

a8ed

ea5c7

48ff

1d

a

Blo

od

An

dT

hu

nd

erM

asto

don

Lev

iath

an

Sto

ner

Met

al

ad

39177

6917d

e576c4

e43c0

f49a432a2

Cel

ebra

teN

oM

otiv

Dia

gra

mfo

rH

eali

ng

Pu

nk

150b

40c

a6c8

f4ed

9a47fc

ebf7

e592544

Ch

eek

toC

hee

kO

scar

Pet

erso

nT

he

Son

gIs

You

Jazz

2477869aa702a0fd

3ca

aa866628b

9cd

e

Ch

itli

ns

con

carn

eK

enny

Bu

rrel

lM

idn

ight

Blu

eJazz

eff46af9

4899c4

4d

650d

546a3b

3d

6e4

b

Cir

ibir

ibin

Har

ryJam

esH

arr

yJam

esJazz

f6f3

a1c4

1008b

bc9

20727741b

4f1

8403

Com

eR

ain

Or

Com

eS

...

Joh

nny

Sm

ith

Th

eS

ou

nd

Of

Th

e..

.Jazz

6453119137792693a92a49e4

069ab

a8b

Con

tras

tsJim

my

Dor

sey

Jim

my

Dors

eyJazz

456a4ec

3ef

6b

e783c3

1842e6

7c3

10f6

8

Cru

sher

Des

troy

erM

asto

don

Rem

issi

on

Sto

ner

Met

al

cbf6

ec05840b

1b

1728d

80177cd

6a9ec

d

Dow

nst

airs

Ken

ny

Bu

rrel

lG

uit

ar

Form

sJazz

03e5

9b

223c3

7fc

c6fe

41c4

3c7

73665b

8

Dr.

Jac

kle

Mil

esD

avis

Mil

esto

nes

Jazz

7e0

def

c50ed

55217a09f9

edea

0424a5b

E.V

.Jim

my

Bru

no

Lik

eT

hat

Jazz

2b

8d

0e6

fec5

0d

d29675e1

1544d

9661a4

58

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Lu

cky

Str

eet

Go

Rad

ioL

uck

yS

tree

tP

un

k45b

f0a7

ce11e4

21e9

afb

504640d

5e2

16

Wh

enD

ream

ing

Get

s..

.G

oR

adio

Do

Over

sA

nd

Sec

o..

.P

un

ka8c9

b90

cdc1

ebd

3ec

b69a7a91093b

b1a

Hol

dO

n,

Hol

dT

ight

Lay

men

Ter

ms

Sin

ceL

ast

Dec

emb

erIn

die

001a9636382a78ca

17ea

832b

f59617f1

Hor

nIn

tro

Mod

est

Mou

seG

ood

New

sF

or

Peo

...

Ind

ie328a9a53a8d

4a6fb

d75473ec

f9a26503

Mai

nli

ne

Hot

Wat

erM

usi

cE

xis

ter

Pu

nk

6e2

e8b

c3f3

500fe

0d

085fc

e9aea

8611b

IO

nly

Hav

eE

yes

fo..

.O

scar

Pet

erso

nT

he

Son

gIs

You

D..

.Jazz

65559638e1

0c8

45a9a323b

cfe5

9cb

3cf

I’m

Old

Fas

hio

ned

Joh

nny

Sm

ith

Leg

end

s:Solo

Gu

i...

Jazz

84ab

a68

f896061c4

057657036225d

7c0

Imag

ine

Joh

nL

enn

onL

enn

on

Leg

end

:T

h..

.R

ock

fceb

c7850f3

03c1

53c5

13773fe

301452

InA

Mel

low

Ton

eH

erb

Ell

isA

rriv

al

Dis

c2

Jazz

82f0

6d

ee9cf

a9923c9

3fd

979e3

872a1c

InT

he

Mood

Gle

nn

Mil

ler

Gle

nn

Mil

ler

Jazz

dff

8ee

35a5a8786053ac6

651b

8d

ce017

Iron

Cla

dL

ouH

um

Ele

ctra

2000

Alt

ern

ati

vea47e4

b427f9

116f6

cad

c262af2

5cd

e39

Isle

ofth

eC

hee

tah

Hu

mD

ownw

ard

IsH

eave

...

Alt

ern

ati

ve4ee

e70a7a3b

d0d

97fd

edc2

3b

f5cd

a640

Op

ener

Jim

my

Eat

Worl

dS

ingle

sIn

die

539c2

01cb

df9

9d

8c6

aa7fd

55f7

8af9

9d

Th

inkin

g,T

hat

’sA

llJim

my

Eat

Worl

dS

tati

cP

reva

ils

Ind

iec1

7491363a1a57fe

f97e3

35b

841e0

3c8

Look

for

the

Sil

ver.

..H

erb

Ell

isA

rriv

al

Dis

c1

Jazz

b3c7

8a9

f107a9c1

d6b

9a61d

9174d

91b

e

Dow

nto

wn

(Intr

o)L

uce

roW

om

en&

Work

Alt

-Cou

ntr

yce

6d

348

9c7

d5861184642e8

b6d

f6ed

4c

Lu

shL

ife

Joe

Pas

sV

irtu

oso

No

4D

isc

1Jazz

fb1155d

c0ce

c345418f5

1d

5c8

e691d

b3

Blu

rM

ake

Do

An

dM

end

Eve

ryth

ing

You

Ev..

.P

un

ka9e3

f4d

9d

86e6

cdc8

469b

a39899d

2397

Bla

ckT

ongu

eM

asto

don

Th

eH

unte

rS

ton

erM

etal

1678231282d

d253ef

c2cf

264261645f8

Mom

ents

Pas

sH

otW

ater

Mu

sic

Moon

pie

sF

or

Mis

fits

Pu

nk

c95b

f50a1cb

a144c2

ad

40c5

211061e3

0

59

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

New

Inst

rum

enta

lH

otW

ater

Mu

sic

Nev

erE

nd

erD

emos

Pu

nk

4b

b13b

07af7

69b

a313f5

e50c5

6ee

6cf

c

Nig

ht

and

Day

Joe

Pas

sV

irtu

oso

Jazz

99d

b2a8b

625275fa

2d

9750f7

04d

078ae

Nos

talg

iaN

oM

otiv

An

dT

he

Sad

nes

sP

...

Pu

nk

9ff

1e3

d318d

3b

dae4

24d

c08e3

e6ad

30f

Ob

livio

nM

asto

don

Cra

ckth

eS

kye

Sto

ner

Met

al

6eb

21ab

c6f4

629f9

79eb

43b

b115ec

5e5

Orc

hes

trio

nP

atM

eth

eny

Orc

hes

trio

nJazz

54c5

a526e4

6a85f6

e04388171d

d50f0

a

Pap

’sB

lues

Her

bE

llis

Noth

ing

Bu

tth

eB

...

Jazz

7a0332c6

2fd

406519954646cf

eb34c9

1

Pla

net

Of

Sou

nd

Pix

ies

Dea

thT

oT

he

Pix

ies

Alt

ern

ati

ve05d

37c2

252c9

758e2

0aab

bfc

5e2

81247

Pla

yM

ike

Ste

rnP

lay

Jazz

770ab

c304d

d540713b

dc3

0c3

7270ac1

9

Poi

son

Hot

Wat

erM

usi

cT

he

New

Wh

at

Nex

tP

un

k9d

bb

d1b

46b

6013f3

2b423dc0

566c2

7d

d

Rem

edy

Hot

Wat

erM

usi

cC

au

tion

Pu

nk

ba4956d

ba1a0673a719c1

0422e6

7351a

Sh

ine

Her

bE

llis

Th

eC

on

cord

Jazz

...

Jazz

fc34c9

cae4

4c6

06a7ee

3f0

6e9

caa7662

Son

ghou

seK

evin

Eu

ban

ks

Sh

ad

owP

rop

het

sJazz

bd

04468e3

ba5d

960554d

bec

3682d2ad

8

Sou

thE

ast

Fir

stH

otW

ater

Mu

sic

No

Div

isio

nP

un

ka629874a67ce

03424cd

98b

ea84584c3

f

Sto

p!

Jan

e’s

Ad

dic

tion

Rit

ual

de

loh

ab

i...

Alt

ern

ati

ved

172248

6ee

7240b

9e3

9369652f8

e5c4

4

Tec

hn

oJoh

nS

cofi

eld

Sti

llW

arm

Jazz

1a78842d

a6cc

94b

4f1

65320b

e1b

c89f5

Tel

lY

ouW

hat

Joh

nS

cofi

eld

Lou

dJazz

Jazz

2760cc

66254b

942704962d

9d

3d

676301

Tem

pta

tion

Wai

tsG

arb

age

Ver

sion

2.0

Alt

ern

ati

ve91fd

2ca

ba6f4

8f0

24b

7b

b31ea

20776e1

Th

eP

hin

eas

Tra

ne

Pat

Mar

tin

oT

hin

kT

an

kJazz

70d

d674ca

3a11607352e8

06d

d2f5

39c9

Th

eS

oun

dO

fS

ilen

ceP

atM

eth

eny

Wh

at’

sIt

All

Ab

ou

tJazz

4097e5

a9b

8ef

6757d

2b

48e4

1028f4

977

Th

eS

tory

Tel

ler

Kev

inE

ub

anks

Th

eS

earc

her

Jazz

3502f7

daff

6115b

5d

167c4

52a84521b

0

60

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Th

eV

isit

Pat

Mar

tin

oF

ootp

rints

Jazz

898911e0

8e2

fbf2

031d

3220cc

eb2d

6b

b

Th

eW

olf

IsL

oos

eM

asto

don

Blo

od

Mou

nta

inS

ton

erM

etal

d39d

268d

1543a14401cb

f6d

bc0

3d

2b

3f

Ton

y’s

Gir

lL

aym

enT

erm

sA

nIn

trod

uct

ion

Ind

iee2

9b

82b

6ef

dcf

4e7

a1cd

e5e2

6171e5

9f

Tra

nsl

oca

tion

Hot

Wat

erM

usi

cF

ore

ver

an

dC

ounti

ng

Pu

nk

5c9

83b

b4488ed

47828866512c0

2ee

a14

Tu

mb

leH

ome

Mik

eS

tern

Wh

oL

etT

he

Cats

...

Jazz

eb1e6

87754b

7d

50ab

596fa

a8b

02504b

c

Tu

rnin

gP

oint

(Par

t1)

Kev

inE

ub

anks

Tu

rnin

gP

oin

tJazz

ac1

c02a1d

358659d

2d

f8b

821a7907a68

Wab

ash

III

Joh

nS

cofi

eld

Tim

eon

My

Han

ds

Jazz

b9cf

0e1

97e5

ecc6

d527b

7069e6

6a447a

Wal

k,

Don

’tR

un

!Joh

nny

Sm

ith

Walk

,D

on

’tR

un

Jazz

6a630423b

6c4

04f1

4b

911d

c318fd

f604

Wh

ere

Or

Wh

enJoh

nny

Sm

ith

Moon

light

InV

erm

ont

Jazz

9e8

648757e5

3fa

b0b

efe2

a4f3

83315b

6

Wil

ma’

sR

ainb

owH

elm

etB

etty

Alt

ern

ati

vec7

69711927c2

f38057c5

811a90a48587

Ble

wN

irva

na

Ble

ach

Alt

ern

ati

veb

84d

f3fe

188586c7

9804cb

068d

67a373

Bod

omA

fter

Mid

nig

ht

Ch

ild

ren

ofB

od

om

Foll

owT

he

Rea

per

...

Met

al

98d

78b

1f0

9b

a0435ef

1ad

a4ec

d955260

AM

ovie

Scr

ipt

En

din

gD

eath

Cab

for

Cu

tie

Th

eP

hoto

Alb

um

Ind

ied

550fa

d4b

a71e4

c04c9

8a7e9

e30b

1753

Act

Ap

pal

led

Cir

caS

urv

ive

Ju

turn

aIn

die

8e2

8361c1

b1b

a9fc

7ec

97b

c837508e1

6

Alb

atro

ssC

orro

sion

Of

Con

for.

..D

eliv

eran

ceS

ton

erM

etal

7103d

feed

579b

8a2a65710e9

2960e3

a2

All

Ner

eid

sB

ewar

eC

hio

dos

All

’sW

ell

Th

at

E..

.P

un

k3ad

2d

bcd

b85cc

28139a98346eb

b44ad

f

All

the

Th

ings

You

Are

Ch

arli

eP

arker

Yard

bir

dS

uit

eD

i...

Jazz

9038e5

6d

51702440fe

704aeb

fcc0

1fe

5

Alw

ays

&N

ever

Coh

eed

and

Cam

bri

aG

ood

Ap

oll

o,

I’m

...

Pu

nk

886b

6fd

96e0

4ab

330529a4c3

ef4c3

d28

Am

eric

anG

irls

Cou

nti

ng

Cro

ws

Hard

Can

dy

Alt

ern

ati

ve7c3

6a3ce

1e0

4760b

4b

ad

18b

b3ab

41ae7

An

gels

ofth

esi

len

ces

Cou

nti

ng

Cro

ws

Rec

over

ing

Th

eS

a..

.A

lter

nati

ve68af5

f9408658ec

12432a15737f7

842e

61

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

An

oth

erD

ayD

ream

Th

eate

rIm

ages

And

Word

sP

rog.

Met

al

dd

9709a7b

778c8

785fc

14b

00b

5088c6

6

Are

You

Dea

dY

et?

Ch

ild

ren

ofB

od

om

Are

You

Dea

dY

et?

Met

al

5b

1a67e

73d

68d

39ad

3b

1d

4a1906b

d9b

b

Bad

Boy

(Ori

gin

alm

ix)

Eri

cC

lap

ton

Eri

cC

lap

ton

Del

u..

.B

lues

eeb

eecb

6f3

a0765ec

7179098d

a1d

5909

Bad

Boy

Eri

cC

lap

ton

Eri

cC

lap

ton

Del

u..

.B

lues

b823ff

29520d

91e1

b18e2

45010d

17b

ee

Bef

ore

You

Acc

use

Me

Eri

cC

lap

ton

Un

plu

gged

Blu

es7cf

a5cf

21363336d

bf2

159128e3

1a3a6

Ben

son

Hed

ges

Fu

n.

Aim

an

dIg

nit

eIn

die

745f7

989345ac5

8a3a7842af5

f52ff

ed

Cal

idos

cop

ico

Diz

zyG

ille

spie

Afr

o-C

ub

an

Jazz

M..

.Jazz

83d

5d

850183356a0287b

88019747a4fd

Cau

ght

InA

Web

Dre

amT

hea

ter

Aw

ake

Pro

g.

Met

al

29e3

c87a4c1

7f0

8fb

5cd

4d

47b

1a5c7

1c

Ch

oos

eth

eO

ne

Wh

o..

.C

opel

and

InM

oti

on

Ind

ie908f6

e6667b

894cd

0cc

b1d

b25f1

1c0

d4

Dye

dO

n8

Ch

rist

ieF

ront

Dri

ve

Anth

olo

gy

Ind

ied

5c6

20b

9e7

b4347f2

b11ae1

87b

aaf4

81

Cod

esan

dK

eys

Dea

thC

abfo

rC

uti

eC

od

esan

dK

eys

Ind

iea8c2

f618896cf

a1fe

6230660ee

6e4

f26

Com

eB

ack

Hom

eP

ete

Yor

nD

ayI

Forg

ot

Alt

ern

ati

veb

a63d

bb

1c9

b87c9

d3cb

5a27280288d

6f

Com

fort

able

Lia

rC

hev

elle

Won

der

Wh

at’

sN

ext

Met

al

c38350e2

f012ef

1f5

915b

420b

02e5

ad

6

Tes

tin

gT

he

Str

ong

...

Cop

elan

dB

enea

thM

edic

ine

...

Ind

ie2b

c8c7

b34ef

6ab

48016525e2

d7ec

c679

Dam

ned

For

All

Tim

eC

orro

sion

Of

Con

for.

..B

lin

dS

ton

erM

etal

d66203b

b71b

79af0

48822d

94d

43160c4

Fly

On

Th

eW

ind

scre

...

Dep

ech

eM

od

eB

lack

Cel

ebra

tion

New

Wav

e19b

150f

b6eb

e6b

07e5

c4502d

c94fb

04c

Sh

ine

Dep

ech

eM

od

eE

xci

ter

New

Wav

efb

02657

3d

1e5

3273a589cb

a2459a4b

0f

Th

eL

ove

Th

ieve

sD

epec

he

Mod

eU

ltra

New

Wav

e028960d

2ef

a586e8

e8b

407a24b

b809ed

Wal

kin

gin

My

Sh

oes

Dep

ech

eM

od

eS

on

gs

of

Fait

han

...

New

Wav

e416b

a82

23d

fe81950369aec

3b

638e8

e1

Din

g-D

ong

Dad

dy

of..

.C

her

ryP

opp

in’

Dad

die

sZ

oot

Su

itR

iot

Sw

ing

00343b

8fc

fa1ea

f306f8

d711c5

cc4d

83

62

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Cat

sin

aB

owl

Din

osau

rJr.

Din

osa

ur

Alt

ern

ati

ve9b

ca35b

c7b

8317a705c7

d8919ec

6a64a

Ina

Jar

Din

osau

rJr.

Foss

ils

Alt

ern

ati

vec1

372829957d

c7fe

2ce

48f9

55b

b201b

e

No

Bon

esD

inos

aur

Jr.

Bu

gA

lter

nati

vefd

4a32f

cc83d

3248ef

1ce

755b

f27d

f4e

Sid

eway

sD

inos

aur

Jr.

Wh

ate

ver’

sC

ool

W..

.A

lter

nati

ve70fe

3a75c5

cd85b

88e7

1a8ec

f861fc

da

Wh

atE

lse

IsN

ew[L

...

Din

osau

rJr.

ID

on

’tT

hin

kS

oA

lter

nati

ve898cc

f7b

6709365f2

0d

c51ee

7d

5b

9d

23

God

Wil

lin

gD

rop

kic

kM

urp

hys

Th

eM

ean

est

Of

Ti.

..P

un

k2e0

4c8

d1af0

e52c2

2b

70c0

8219f7

e37b

Du

de,

you

hav

en

oi.

..H

arri

son

Ber

ger

on

Harr

ison

Ber

ger

on

Pu

nk

9315a9651b

a7e6

042d

10d

f6ef

eeb

78fa

Eve

nF

low

Pea

rlJam

Ten

(Del

uxe

Ed

itio

n)

Alt

ern

ati

ve08b

621e

9480b

f6d

2275b

7c8

22098ae1

4

Th

eO

nly

Mon

ent

We

...

Exp

losi

ons

InT

he

Sky

Th

eE

art

hIs

Not

...

Ind

ie0502b

fc75ff

d552f1

dd

360d

398a66c2

7

Wel

com

e,G

hos

tsE

xp

losi

ons

InT

he

Sky

All

of

aS

ud

den

I...

Ind

ie8f8

78a5d

e9aa063f1

844ed

7f3

6d

68027

I’m

Not

Afr

aid

Fac

eT

oF

ace

Don

’tT

urn

Aw

ayP

un

k9025eb

6599ef

a5d

994b

d219959ef

b85a

Pri

de

War

Fu

rth

erS

eem

sF

ore

ver

Hop

eT

his

Fin

ds

Y..

.In

die

5b

d012b

32e6

7f4

49aab

d762456f0

1ed

a

Fu

ryof

the

Sto

rmD

rago

nF

orce

Son

icF

ires

torm

Met

al

85642501f7

6927f4

d7215856d

bf2

7a44

Hap

py

En

din

gsC

ounte

rfit

Su

per

Am

use

men

tM

...

Ind

ieac5

9e9

f2a12177f1

2143c3

cc75f9

44e0

Hid

eN

oth

ing

Fu

rth

erS

eem

sF

ore

ver

Hid

eN

oth

ing

Ind

ie23d

a715

407d

c4a61d

30122d

f6986f8

b4

Hol

eT

oF

eed

Dep

ech

eM

od

eS

ou

nd

sO

fT

he

Un

i...

New

Wav

e17fb

0f9

21a438a56b

c946c2

bae1

9cb

ed

IC

an’t

Get

Sta

rted

Diz

zyG

ille

spie

Diz

zyA

tmosp

her

eJazz

5f7

df3

2641488f3

5e9

b209ee

18a0307a

IF

eel

Bet

ter

Fri

ghte

ned

Rab

bit

Th

eM

idn

ight

Org

a..

.In

die

78186b

cfcf

11d

7b

a84c8

68a1d

bee

7f2

2

IW

asB

orn

InT

he

Dar

kF

irew

orks

Gosp

elP

un

kd

b3ed

a2c3

07024b

c54196f2

8d

2fa

b13c

InM

yP

lace

Col

dp

lay

AR

ush

Of

Blo

od

T..

.In

die

ae1

17ec

4d

5ae5

c3ff

6f3

926e1

976681a

63

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

So

Wh

atE

lse

IsN

owJ

Mas

cis

Mart

in+

Me

Alt

ern

ati

ve432b

eff8d

c41987ce

f1ad

8af3

7a6023c

Joh

nth

eR

evel

ator

Dep

ech

eM

od

eP

layin

gth

eA

ngel

New

Wav

e62a8d

9877b

c0fe

258f4

571d

8f1

513c1

a

Kil

lT

he

Mu

sic

Eve

ryT

ime

ID

ieG

utt

erP

hen

om

enon

Met

al

e60a5705d

6f2

e84fd

46cd

12f1

a76b

bd

b

Les

son

sD

amie

raM

US

ICIn

die

ba640d

2cb

1fd

4d

20e3

798b

5c8

1cd

d775

Lig

htn

ess

Dea

thC

abfo

rC

uti

eT

ran

satl

anti

cism

Ind

ied

b35716ff

e8fe

f1709b

bd

2ed

cea65a8b

Lon

gW

hip

,Big

Am

eric

aC

orro

sion

Of

Con

for.

..W

iseb

lood

Sto

ner

Met

al

5b

6c2

af2b

cc1a795211a4ab

04d

b69935

Low

Foo

Fig

hte

rsO

ne

By

On

eA

lter

nati

ve9123574d

7d

5642cc

c0a7a2f6

5a6e5

2cd

Mil

lsto

ne

Bra

nd

New

Th

eD

evil

An

dG

od

...

Pu

nk

64a67b

1159aa6b

271938b

c2d

833428a6

Mrs

.P

otte

r’s

Lu

llab

yC

ounti

ng

Cro

ws

Th

isD

eser

tL

ife

Alt

ern

ati

ve8cb

aab

8e2

439d

21ce

8d

337f3

bad

e8958

Mu

sic

Mu

sic

Eye

dea

&A

bil

itie

sF

irst

Born

Hip

-Hop

f6d

f48e

5d

9ed

495a7e2

e8c0

11aec

808d

Mu

sic

Now

Fri

ghte

ned

Rab

bit

Sin

gT

he

Gre

ys

Ind

ie33d

a5a6

d77e0

903c5

492e5

f57b

51436a

My

Mel

anch

oly

Bab

yD

izzy

Gil

lesp

ieB

irk’s

Work

sJazz

68d

75f6

2c8

4a334f4

20e4

962a1728792

N.S

.U.

Cre

amT

he

Bes

tO

fC

ream

...

Rock

6d

453d

0c9

4f7

3c2

dd

686e3

7274323890

New

Yea

rC

ounte

rfit

Fro

mF

inis

hto

St.

..In

die

ae3

431d

01ad

d91b

85a913375a7526567

Nob

ody

Kn

ows

Th

eT

r...

Diz

zyG

ille

spie

Imp

rom

ptu

Jazz

f42aa40d

2c5

4c7

4d

6e3

8089b

037303fa

Not

hin

gW

ith

You

Des

cen

den

tsC

ool

To

Be

You

Pu

nk

4a83c6

e14575e3

c02758a05c8

063f5

a5

Ob

livio

nO

cean

Pai

nO

fS

alva

tion

Th

eP

ain

ful

Ch

ron

...

Pro

g.

Met

al

71b

0806

1407f7

e0f6

7f7

c6d

5c5

119213

Off

Bro

adw

ayE

very

Tim

eI

Die

Hot

Dam

n!

Met

al

f39af5

f7724f7

a35d

50afc

bfa

7866b

15

Om

aha

Cou

nti

ng

Cro

ws

Au

gu

stan

dE

very

t...

Alt

ern

ati

ve0683ce

0a8e6

1aa411b

cf634ad

a78e5

e4

Ou

tof

Now

her

eC

har

lie

Par

ker

Yard

bir

dS

uit

eD

i...

Jazz

e9aca

351c6

4430e9

ab

5639508b

5d

4b

55

64

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Ove

rtu

re19

28D

ream

Th

eate

rM

etro

poli

sP

t.2

S..

.P

rog.

Met

al

ce125c9

6fd

2079b

3114d

f6778ef

18802

Par

anoi

dO

pio

idC

orro

sion

Of

Con

for.

..In

Th

eA

rms

Of

God

Sto

ner

Met

al

b686fe

5d

2b

1e2

93b

dd

599969cd

3139e7

Pre

sid

ent

ofW

hat

?D

eath

Cab

for

Cu

tie

Som

eth

ing

Ab

ou

tA

...

Ind

iee6

ec56fb

d38c7

cc3ca

a8d

ebd

f7c4

bb

7d

Pri

son

erD

okke

nB

ack

For

Th

eA

ttack

Met

al

9b

d0af2

39262b

f538e9

3c9

9d

ebc2

9b84

Ray

’sId

eaD

izzy

Gil

lesp

ieC

ool

Bre

eze

Jazz

ca07d

37ee

5ff

89c4

8054e2

e7b

103971f

Red

IsT

he

New

Bla

ckF

un

eral

For

AF

rien

dS

even

Way

sT

oS

cr..

.P

un

kfb

4179a

64e7

497e3

d6d

eb9997b

907b

c2

Rock

etD

efL

epp

ard

Hyst

eria

Met

al

c18cc

3c9

b59869294aacc

13f1

64c0

070

Sal

tP

eanu

tsD

izzy

Gil

lesp

ieS

alt

Pea

nu

tsJazz

190c3

7e0

431922fe

68aa56a70e6

198c5

Say

ItT

oM

yF

ace

Ch

erry

Pop

pin

’D

ad

die

sK

ids

on

the

Str

eet

Sw

ing

0e8

35ae0

6b

34e1

67a4f4

ed7692e8

235f

Scr

eam

ing

Infi

del

itie

sD

ashb

oard

Con

fess

ion

al

Th

eP

lace

sY

ou

Ha..

.In

die

c58e0

9ab

64623b

24fe

a3485b

daca

e60f

Sh

ine

Fad

edG

rey

AQ

uie

tT

ime

Of

D..

.P

un

k38d

332f

a0c3

c7ee

4b

d6626463acd

34b

6

Six

pou

nd

erC

hil

dre

nof

Bod

om

Hate

Cre

wD

eath

roll

Met

al

3071674b

36b

851a7fc

490fc

66a8c8

055

Sn

owfa

llE

mil

yR

emle

rE

ast

To

Wes

Jazz

0e3

ab

2980763afe

8a23aff

bd

d14b

6387

Sock

able

Fac

eC

lub

Ch

erry

Pop

pin

’D

ad

die

sR

ap

idC

ity

Mu

scle

...

Sw

ing

c292749478c4

2e4

96e7

5cf

746489cd

0e

Som

eN

ights

Fu

n.

Som

eN

ights

Ind

ie551864b

c887cc

3b

2664599166c0

f70b

e

Sou

nd

trac

kT

oT

he

Rid

eF

airw

eath

erIf

Th

eyM

ove.

..K

i...

Pu

nk

2c0

a22d

04893338f2

343ef

e75200d

6ea

Sp

ook

yG

irlf

rien

dE

lvis

Cos

tell

oW

hen

IW

as

Cru

elR

ock

7074c7

5ed

658eb

53325176b

e532ce

29c

Sw

imU

nti

lY

ouC

an’.

..F

righ

ten

edR

ab

bit

Th

eW

inte

rof

Mix

...

Ind

ie87fc

ae8

4ef

cef8

0e6

5eb

ef7c2

62d

50c4

Sw

ingi

ng

Wit

hT

iger

...

Ch

erry

Pop

pin

’D

ad

die

sS

ou

ldC

ad

dy

Sw

ing

bc0

41fe

323b

7108d

8c3

a3b

ecca

6f2

c30

Tak

eth

e”A

”T

rain

Du

keE

llin

gton

Bes

tof

Du

keE

lli.

..Jazz

ff3d

4f4

153d

ffd

b6a0c4

bee

4aa4b

b833

65

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Tea

rsB

efor

eB

edti

me

Elv

isC

oste

llo

Imp

eria

lB

edro

om

Rock

60a175a366c5

89b

a2f4

6ef

5ce

689669a

Tee

nag

eB

rain

Su

rgeo

nC

her

ryP

opp

in’

Dad

die

sF

eroci

ousl

yS

ton

edS

win

g320629ab

9e9

c7cb

3d

580fb

d97a7c5

cff

Th

eA

nsw

erL

ies

Wit

hin

Dre

amT

hea

ter

Oct

avari

um

Pro

g.

Met

al

99a8139b

fb61fe

6a63afd

bd

e3b

b6bb

28

Th

eB

oat

Chu

ckR

agan

Los

Fel

izIn

die

20b

5378

aa8b

2f8

23e6

5e7

83d

e129e3

10

Th

eB

rad

ley

Fu

rth

erS

eem

sF

ore

ver

Th

eM

oon

IsD

own

Ind

ief9

4107fa

55b

383e9

e1e4

4b

e2ac4

14cb

e

Th

eE

mp

loym

ent

Pag

esD

eath

Cab

for

Cu

tie

We

Hav

eT

he

Fact

s...

Ind

ie708ae6

11b

0116f9

75e8

367f1

af4

0f6

3b

Th

eH

unte

rD

okke

nU

nd

erL

ock

An

dK

eyM

etal

8ea

7a84b

0c5

d805970a75180e9

4f9

6d

6

Th

eS

oun

dF

urt

her

See

ms

Fore

ver

How

To

Sta

rtA

Fir

eIn

die

460813d

3c9

bb

a12ca

4b

5983612542e7

e

Th

eT

ake-

Aw

ayF

ace

To

Fac

eH

owT

oR

uin

Eve

ry..

.P

un

k5121493cf

260497c7

16a8601acf

282ae

Th

isY

ear’

sG

irl

Elv

isC

oste

llo

Th

isY

ears

Mod

elR

ock

5c8

75951fe

5b

c831c8

8474eb

81d

2afd

a

Val

ley

Of

Th

eD

amn

edD

rago

nF

orce

Vall

eyO

fT

he

Dam

ned

Met

al

523b

db

757b

eb279e1

e65c9

fba1147648

Wai

tin

gF

orA

Gir

l..

.F

orei

gner

Th

eD

efin

itiv

eC

o..

.R

ock

5b

fb480f6

50d

fbfa

0fe

f2f1

034a60c7

2

Yas

min

Th

eL

ight

Exp

losi

ons

InT

he

Sky

Th

ose

Wh

oT

ell

Th

...

Ind

ie5fd

43ae

c7204b

09ec

7d

3194fa

deb

4f8

b

You

rH

and

InM

ine

(...

Exp

losi

ons

InT

he

Sky

Fri

day

Nig

ht

Lig

hts

Ind

ied

7d

111769c3

3cc

4d

793240cd

7c6

8725c

An

dro

gyny

Gar

bag

eb

eau

tifu

lgarb

age

Alt

ern

ati

ve50ad

a5a

80f7

85c6

4c2

95cc

e23d

d7311d

Bac

kyar

dS

ku

lls

Fri

ghte

ned

Rab

bit

Ped

estr

ian

Ver

seIn

die

5221ac8

710765d

bb

549c3

2866aca

8a4c

Blo

od

dru

nk

Ch

ild

ren

ofB

od

om

Blo

od

dru

nk

Met

al

b42f9

3347e6

d9ed

6c2

53235e9

1814d

d0

Cem

eter

ies

Of

Lon

don

Col

dp

lay

Viv

aL

aV

ida

Ind

ie0ae0

0c2

6861a1af4

ed5e9

df7

80910d

a6

AD

iam

ond

and

aT

eth

erD

eath

Cab

for

Cu

tie

Th

eO

pen

Door

EP

Ind

ie2b

29540

4f9

bb

d96fe

7f7

4093cd

307b

53

IW

ill

Pos

sess

You

r...

Dea

thC

abfo

rC

uti

eN

arr

owS

tair

sIn

die

4a94eb

c76eb

96b

85a611ea

d94b

0d

42d

5

66

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Th

eT

hin

gsY

ouS

aid

Dep

ech

eM

od

eM

usi

cF

or

Th

eM

ass

esN

ewW

ave

2526969d

5b

7f7

f50b

36b

d52a18a9b

a65

Now

Eye

dea

&A

bil

itie

sE

&A

Hip

-Hop

1168eb

29fa

76f4

49d

8d

7b

25243370b

24

Sp

inC

ycl

eE

yed

ea&

Ab

ilit

ies

By

Th

eT

hro

at

Hip

-Hop

95b

bee

bcb

13164815aec

c66b

01592fb

7

Goi

ng

Ou

tIn

Sty

le..

.D

rop

kic

kM

urp

hys

Goin

gO

ut

InS

tyle

Pu

nk

23579b

0c2

ba9137cc

b8531872a8d

e66c

Hu

man

Qu

alit

ies

Exp

losi

ons

InT

he

Sky

Take

Care

,T

ake

C..

.In

die

5825a8b

04754d

ba5b

6449ed

d295a1472

IS

omet

ime

Wis

hI

W..

.D

epec

he

Mod

eS

pea

k&

Sp

ell

New

Wav

e4599580ca

86ac9

640223f7

b8215013ef

It’s

Not

All

Ab

out

You

Fac

eT

oF

ace

Lau

gh

Now

...L

au

gh

...

Pu

nk

dd

2001395a74223e7

427fc

d6b

4ff

402a

Mon

um

ent

Dep

ech

eM

od

eA

Bro

ken

Fra

me

New

Wav

e7f5

a80298d

954d

29604b

ccafb

3081c9

f

Mor

eT

han

aP

arty

Dep

ech

eM

od

eC

on

stru

ctio

nT

ime.

..N

ewW

ave

1f5

b29c

d5ac4

c1b

9f8

ba263ce

a23f6

22

Rad

ioC

hri

stie

Fro

nt

Dri

ve

Ch

rist

ieF

ront

Dri

ve

Ind

ie8127c2

a615d

e6141e7

77a2859400a280

Ru

nB

aby

Ru

nG

arb

age

Ble

edL

ike

Me

Alt

ern

ati

ve401fa

67cd

2611257ee

fccf

8f8

516fb

64

Sh

owM

eM

ary

(XF

MR

...

Cat

her

ine

Wh

eel

Way

dow

n[1

]A

lter

nati

ve29d

2236

4f4

485e9

5a1a4d

671f9

6ed

9f6

Sn

owA

nd

Lig

hts

Exp

losi

ons

InT

he

Sky

How

Str

an

ge,

Inn

o..

.In

die

ea741ea

0f6

1a0ed

f07c0

3d

d9b

ee6107b

Sta

rtA

gain

Cou

nti

ng

Cro

ws

Un

der

wate

rS

un

shin

eA

lter

nati

ve5366f1

dc9

2c0

fe2ec

8d

07d

6f2

28b

e3fb

Why

You

’dW

ant

To

L..

.D

eath

Cab

for

Cu

tie

Th

eJoh

nB

yrd

E.P

.In

die

a9622b

8e5

cd3ef

e507fe

af4

1cc

e3e0

9d

3C

hor

ds

Con

stel

lati

onof

Cars

Con

stel

lati

on

of

...

Ind

ie1d

7e3

f4c8

0b

264e8

6b

20d

97f1

38d

0953

Blu

e’n

’B

oog

ieD

izzy

Gil

lesp

ieJazz

Coll

ecto

rE

d..

.Jazz

bd

922ee

fbce

13a2993cd

6e3

1ca

0d

c114

Hu

rts

Lik

eH

eave

nC

old

pla

yM

ylo

Xylo

toIn

die

81179f4

fccb

606997f2

23937e8

6143f5

Th

eG

rey

Man

Cop

elan

dY

ou

Are

My

Su

nsh

ine

Ind

ie77a0e7

cb790f6

2e6

47d

bc8

aac8

14ce

e3

Tec

hn

icol

orG

irls

Dea

thC

abfo

rC

uti

eF

orb

idd

enL

ove

E.P

.In

die

ed7967e

573592a991834b

232f2

b550b

e

67

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Pu

ke+

Cry

Din

osau

rJr

Gre

enM

ind

Alt

ern

ati

vece

b2613

e2d

3627b

a65a7e2

57a152223c

Eve

ryth

ing

toE

very

one

Eve

rcle

arS

oM

uch

for

the

A..

.A

lter

nati

ve9fc

ae8

756a683b

eb88a14c2

f57d

44086

IK

now

You

Wel

lF

ace

To

Fac

eB

igC

hoic

eP

un

ka7ab

7a6

844c3

209d

308e0

0d

c2d

4043a5

IW

ant

Fac

eT

oF

ace

Liv

eP

un

kd

592c3

961690102a4429666957c6

6780

InH

arm

sW

ayF

ace

To

Fac

eIg

nora

nce

isB

liss

Pu

nk

8cf

60f5

26939b

3c8

93ee

7ad

10b

695e1

b

Ou

tof

Focu

sF

ace

To

Fac

eR

eact

ion

ary

Pu

nk

1f0

21878164d

df3

00cff

c3011762100f

Wal

kT

he

Wal

kF

ace

To

Fac

eF

ace

To

Face

Pu

nk

b4e4

f247c6

99f1

7a84ce

ebc5

94ed

04b

a

Wel

com

eB

ack

To

Not

...

Fac

eT

oF

ace

Th

ree

Ch

ord

sA

nd

...

Pu

nk

0139690a26f6

3c3

5a2e7

5278a67b

961c

Com

eA

rou

nd

Fir

ewor

ks

All

IH

ave

To

Off

...

Pu

nk

03ec

f00e7

e0afd

5b

aa8003b

6d

623456a

Pu

mp

edU

pK

icks

Fos

ter

Th

eP

eop

leT

orc

hes

Ind

ie0f8

fd09

1754a4633e1

c20fa

9a9474ce

7

Res

cue

Tra

ined

Fu

rth

erS

eem

sF

ore

ver

Pen

ny

Bla

ckIn

die

68b

256d

c0c9

65f4

c10c5

fbaa867ef

9fd

Gas

olin

eC

ath

erin

eW

hee

lW

ishvil

leA

lter

nati

ve5f7

852412d

8ce

cdd

19fc

8ef

6c6

1e5

de9

He

Lov

esan

dSh

eL

oves

Ell

aF

itzg

erald

an

d...

Ell

a&

Lou

isS

ing..

.Jazz

22905ef

5a9e4

e305d

60b

0b

8e3

c7e4

f87

Her

eW

eG

oA

gain

Eve

rcle

arS

on

gs

Fro

man

Am

e...

Alt

ern

ati

vee5

3b

6d

a8a35c0

b1b

201725a932657265

Her

oin

Gir

lE

verc

lear

Sp

ark

lean

dF

ad

eA

lter

nati

ved

f5ca

a3f7

fc96e0

d03270f9

048127147

How

Inse

nsi

tive

Em

ily

Rem

ler

Ret

rosp

ecti

veV

ol.

..Jazz

e85d

221

75cc

217a0103b

0b

efc6

5d

82a8

InK

eep

ing

Sec

rets

...

Coh

eed

and

Cam

bri

aIn

Kee

pin

gS

ecre

t...

Pu

nk

5f5

50e6

ee34277d

0b

06059af4

9879ab

4

Lov

eIs

Her

eT

oS

tay

Ell

aF

itzg

erald

an

d...

Th

eB

est

Of

Ell

a..

.Jazz

19ee

dd

781e4

9085108b

e3e6

886fa

a40f

No

Wor

ldfo

rT

omor

row

Coh

eed

and

Cam

bri

aG

ood

Ap

oll

oI’

mB

...

Pu

nk

35ca

c2568267a7fd

48d

286c8

77a47d

10

Nu

nca

Mai

sE

mil

yR

emle

rR

etro

spec

tive

Vol.

..Jazz

1536a82cb

cd217576ef

4cc

42cf

45d

53a

68

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Qu

eer

Gar

bag

eG

arb

age

Alt

ern

ati

vee0

967550e0

b17837717e1

7ef

37a5b

999

Sic

kan

dT

ired

Eve

rcle

arW

orl

dO

fN

ois

eA

lter

nati

ved

672cb

1b

b11709b

c0c9

5163d

877290e0

Th

eJit

terb

ug

Wal

tzF

ats

Wal

ler

Port

rait

Vol.

2-

...

Jazz

cf8244f7

bab

5f5

2ae5

fe3aa57fe

5e6

35

Wh

enD

ayIs

Don

eD

jan

goR

ein

hard

tT

he

Bes

tof

Dja

ng..

.Jazz

ded

ff1fd

9fe

af9

3419e4

8ee

1326104ff

Wh

olly

Cat

sC

har

lie

Ch

rist

ian

Th

eO

rigin

al

Gu

it..

.Jazz

6b

28142

d120417034e9

2651b

442f2

f58

Wh

atIf

Col

dp

lay

X&

YIn

die

6b

4a699

74a92109ad

762252e6

9cc

60ac

Sh

ovel

Kn

ock

out

Ch

ild

ren

ofB

od

om

Rel

entl

ess,

Rec

kl.

..M

etal

f803c7

3f1

0d

75e6

a10779d

84b

b19fe

5c

At

the

Bot

tom

Bra

nd

New

Dais

yP

un

kfb

991d

503ff

1450b

962c7

5d

325d

ecec

8

B-B

oys

Mak

in’

Wit

h..

.B

east

ieB

oys

Ill

Com

mu

nic

ati

on

Hip

-Hop

fbd

5b

25c6

ea3f1

f33ff

dcc

c39a0ff

f1c

Bes

tD

ays

Blu

rT

he

Gre

at

Esc

ap

eA

lter

nati

ve94d

4d

39cc

7886b

7d

c3fc

92970318285b

Bla

ckM

etal

lic

Cat

her

ine

Wh

eel

Fer

men

tA

lter

nati

veb

ccc9

59a8202fd

3017e0

447d

eafd

d90f

Sta

nd

ing

InT

he

Doo.

..B

obD

yla

nT

ime

Ou

tO

fM

ind

Folk

2afe

5e2

ec48aae2

0b

52349e4

45564f3

0

Bom

bB

ush

Six

teen

Sto

ne

Alt

ern

ati

ve1f4

5c9

a12a9f7

33c5

a5e1

26d

d5cc

b350

Eu

lali

a,E

ula

lia

Bra

idM

ovie

Mu

sic

vol.

Tw

oIn

die

742ee

5ff

71d

b928af3

8d

6d

9671452e0

7

Min

uet

Bra

idF

ran

kie

Wel

fare

B..

.In

die

9ca

08d

e8c9

862a58d

423b

d93b

51d

64f2

Bri

ckB

enF

old

sF

ive

Wh

ate

ver

An

dE

ver.

..In

die

be7

9c1

95d

bb

7f3

538cf

e0364f6

d576c6

bro

ken

dow

np

iece

o...

Bri

anS

etze

r13

Rock

abil

ly5f7

e94f1

fb50b

8599a224543fc

1e9

703

Cou

ntr

yS

adB

alla

dM

anB

lur

Blu

rA

lter

nati

ve0eb

7ad

6ca

06691a285e7

7b

58cd

20ea

2d

Cra

nk

Cat

her

ine

Wh

eel

Ch

rom

eA

lter

nati

vee2

b68c3

8e2

868ce

c31ed

dd

088f1

12771

Del

icio

us

Cat

her

ine

Wh

eel

Ad

am

An

dE

ve-

Me.

..A

lter

nati

ve14218fe

31d

c28ae9

b9410f4

50fa

91e4

6

69

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Slu

dge

feas

tD

inos

aur

Jr.

You

’re

Liv

ing

All

...

Alt

ern

ati

veb

3248a7

460b

b32f8

521b

8175ce

9c4

6a6

Dru

nke

nK

nig

ht

Big

wig

Un

-Mer

ryM

elod

ies

Pu

nk

30f0

af0

9a8364ca

aee

cb4101c5

731211

God

Res

tye

Mer

ryG

...

Bin

gC

rosb

yF

rank

...

Ch

rist

mas

Jazz

335ab

58ad

4e3

a569ce

72e0

2fc

240fa

d2

Hey

You

(Bat

chof

L..

.B

lack

Lab

elS

oci

ety

Son

icB

rew

Met

al

d494aad

b3b

bd

0b

6aef

6386797ac7

80b

2

Hey

,Joh

nny

Par

kF

oo

Fig

hte

rsC

olo

ur

&T

he

Sh

ap

eA

lter

nati

ve2ab

152e

36e0

fc14d

1901625603ace

fbc

Ju

stif

yth

eT

hri

llB

lues

Tra

vel

erS

traig

ht

on

Til

l..

.A

lter

nati

ve036b

eeb

2ea

5b

931d

43c1

4c7

9a226f1

38

Let

ter

Fro

mA

Fri

end

Blu

esT

ravel

erS

ave

His

Sou

lA

lter

nati

ve247c3

a344c2

7b

afa

e64a0d

0aa7fe

f107

Lif

etim

e,A

Bet

ter

Th

anE

zra

Bef

ore

Th

eR

ob

ots

Alt

ern

ati

ve574a42658e7

b235d

bb

f242a66ab

ab

7e5

Lit

tle

Mu

scle

Cat

her

ine

Wh

eel

Hap

py

Day

sA

lter

nati

ve091f6

d9c4

7388a9e4

74302d

70b

027f1

1

Lon

gL

ost

Bet

ter

Th

anE

zra

Fri

ctio

n,

Baby

Alt

ern

ati

ve97676a90d

9b

070189cc

1c2

e2b

8e4

ae3

2

Look

Aro

un

dB

lues

Tra

vel

erF

ou

rA

lter

nati

ve943e7

f1387b

4b

47246462b

ab

1a9482fa

Los

t,N

ight

Bil

lF

rise

llD

isfa

rmer

(Sta

nd

ard

)Jazz

87a4069119ab

91ad

5b

ba07e7

79e9

12b

9

Mes

sB

enF

old

sF

ive

Th

eU

nau

thori

zed

...

Ind

ie1c7

390b

a840644988f5

0a238b

0557301

Mor

eT

han

AF

eeli

ng

Bos

ton

Gre

ate

stH

its

Rock

aa1a5078ce

9c0

a595d

1ab

72e6

151ec

d4

Per

fect

Pit

chB

raid

Mov

ieM

usi

cV

ol.

On

eIn

die

11fc

7661e4

6e0

c9c2

a0a3d

d71b

b76224

Sh

e’s

Cra

fty

Bea

stie

Boy

sL

icen

sed

To

Ill

Hip

-Hop

c3d

751d

e1182d

d7ab

222536ef

b8466d

d

Sig

nO

fL

ife

Bil

lF

rise

llan

d85..

.S

ign

Of

Lif

eJazz

4b

a241c

9b

f07574ad

e5962ef

a59d

fbfc

Sm

ile

Big

wig

Sta

yA

slee

pP

un

k018a656b

5e2

0a7aaf3

5ac7

82b

aea

b139

Sou

ther

nG

rlB

ette

rT

han

Ezr

aD

elu

xe

Alt

ern

ati

ve4830e3

b50e5

2b

15d

223b

dd

c284e1

4605

Ste

pp

inS

ton

eB

lack

Lab

elS

oci

ety

Han

gov

erM

usi

cV

o..

.M

etal

149e0

45fd

7870f7

c029e6

08fe

341d

d8f

70

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Su

icid

eM

essi

ahB

lack

Lab

elS

oci

ety

Mafi

aM

etal

1fc

82f2

1c0

2aa19cc

e1d

72666fd

c87a2

Th

eN

inja

Cac

oph

ony

Sp

eed

Met

al

Sym

ph

ony

Met

al

acb

2e5

f7f5

8cc

3180315560aeb

8803d

b

Bac

kw

ards

Gu

itar

Cat

her

ine

Wh

eel

Ju

dy

Sta

rin

gA

tT

...

Alt

ern

ati

ve7ac8

2837d

e46e4

11e4

21e6

8ec

5b

2c9

bc

Col

lid

eosc

opic

Cat

her

ine

Wh

eel

IW

ant

To

Tou

chY

ou

Alt

ern

ati

ve7e0

21cc

b528cd

95b

5756a340c1

84b

243

Des

cen

din

gB

abe

Cat

her

ine

Wh

eel

Ma

Soli

tud

a[D

isc

1]

Alt

ern

ati

ve6b

ae2

c49d

16516cc

5b

ab

1ab

e0458e4

c5

Div

ers

Bra

idT

he

Age

of

Oct

een

Ind

ie86140658751af7

ece3

1c9

14d

e4e6

c374

Hea

l(L

ive

Xfm

)C

ath

erin

eW

hee

lB

roke

nN

ose

[Dis

c2]

Alt

ern

ati

vefa

92e8

61b

44fa

aea

e243fa

963acc

0941

Kil

lin

gA

Cam

era

Bra

idL

uck

yT

oB

eA

live

Ind

ie4b

03ec

ce71b

e25a068b

12944d

e3d

0116

Mas

ters

Of

War

Bob

Dyla

nT

he

Fre

ewh

eeli

n’

...

Folk

8ab

9c3

6e5

b549d

a62877ee

fc6a8fb

cbd

Mou

thfu

lO

fA

irC

ath

erin

eW

hee

lL

ike

Cats

An

dD

ogs

Alt

ern

ati

veaee

2773d

cb0ef

06a0783e3

de1

53970a9

Nev

erW

ill

Com

eF

orU

sB

raid

Fra

me

&C

anva

sIn

die

d864b

a86b

cbc9

7a82e3

fdff

a5fe

43d

7c

Tex

ture

(Liv

e)C

ath

erin

eW

hee

lB

roke

nN

ose

[Dis

c1]

Alt

ern

ati

ve1c9

389f4

b75547a0aec

e024e9

a38b

502

Th

eQ

uie

tT

hin

gsT

h..

.B

ran

dN

ewT

he

Holi

day

EP

Pu

nk

046fc

264f3

00d

d0a8f6

c9b

657fe

af4

94

Ton

gue

Tw

iste

dC

ath

erin

eW

hee

lC

ran

kA

lter

nati

ve7f6

30e3

afd

ea0e2

40a918514e2

b537e9

Wis

hC

ath

erin

eW

hee

lS

he’

sM

yF

rien

d(E

p)

Alt

ern

ati

ve189350a70199c0

fdb

8c6

d51ef

b0a398a

Alw

ays

Gon

na

Get

Ya

Big

Bad

Vood

oo

Dad

dy

Sav

eM

yS

ou

lS

win

ga96391251a887fc

c7fd

959124c8

7c8

02

Th

eC

all

Of

Th

eJit

...

Big

Bad

Vood

oo

Dad

dy

How

Big

Can

You

Get

Sw

ing

50f7

2f2

9371910c9

1e2

3b

af9

04211e5

c

Blu

esE

tud

eB

rian

Nov

aS

had

owO

fY

ou

rS

mil

eJazz

8e0

0b

0280412149e3

d51a47e0

974a110

En

dO

fA

Cen

tury

Blu

rP

ark

life

Alt

ern

ati

ve1d

7d

6b

309b

77805a45f8

2b

268b

ce5fd

0

You

Are

the

Rea

son

Bra

idC

lose

rT

oC

lose

dIn

die

4fd

5270

fc4797d

e8f2

30a6d

9b

8471d

53

71

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Coff

aro’

sT

hem

eB

ill

Fri

sell

wit

hD

...

Bil

lF

rise

llw

ith

...

Jazz

70ee

aa21093f7

086b

c79e2

9d

0b

602e5

8

Com

eR

ain

Or

Com

eS

...

Bil

lie

Hol

iday

Bil

lie’

sB

est

Jazz

09aae6

e14962d

ab

9b

963059f3

4ea

f7b

0

God

son

Son

gB

ill

Fri

sell

Gon

e,Ju

stL

ike

a..

.Jazz

1390c8

3974256d

8ec

499002e5

011e1

8b

Hap

py

Fee

lin

gB

arn

eyK

esse

lT

oS

win

gor

Not

t...

Jazz

d6b

9a57098ef

4220019d

617a1670d

db

5

Hap

py

Lit

tle

Son

gB

arn

eyK

esse

lS

olo

Jazz

84d

3cc

8c8

84d

6f6

39f5

424cc

f6c2

17f6

ItC

ould

Hap

pen

toY

ouB

arn

eyK

esse

lT

he

Poll

Win

ner

sJazz

57b

517a

19b

c649af8

90f3

3e0

4a5d

58a3

ItT

akes

AL

otT

oL

...

Bob

Dyla

nH

ighw

ay61

Rev

isit

edF

olk

1e2

a0775855425ea

dd

86e3

98137a1747

Kin

gO

fS

win

gB

igB

adV

ood

oo

Dad

dy

Big

Bad

Vood

oo

Dad

dy

Sw

ing

32e1

e622861c7

31d

0422eb

289ff

8580f

Mad

hou

seB

enny

Good

man

Ben

ny

Good

man

Jazz

451b

ee4d

3b

18737e0

2a2611ec

ad

7159d

Mag

gie’

sfa

rmB

obD

yla

nB

rin

gin

gIt

All

B..

.F

olk

d9e9

1d

304ea

0a2b

36d

ff34acb

9b

801d

9

Moz

amb

iqu

eB

obD

yla

nD

esir

eF

olk

854d

47e

870c8

c3f6

4f5

795d

5d

14b

c53c

Sp

anis

hH

arle

mIn

ci..

.B

obD

yla

nA

noth

erS

ide

of

B..

.F

olk

1a2ef

a277ad

42a5ef

ba1794f7

f290ce

2

Sp

rin

gIs

Her

eB

arn

eyK

esse

lT

he

Poll

Win

ner

s..

.Jazz

2c7

46ee

5248a88d

0cf

e244ee

bff

dd

8b

4

To

Be

Alo

ne

Wit

hY

ouB

obD

yla

nN

ash

vil

leS

kyli

ne

Folk

bd

7292a3d

18acb

6ab

7518e1

f47f6

6986

Vis

ion

sof

Joh

ann

aB

obD

yla

nB

lon

de

on

Blo

nd

eF

olk

3d

2c9

0c22a37a3b

b30c2

d9fd

1d

fb988e

Wh

o’s

Th

atC

reep

in’?

Big

Bad

Vood

oo

Dad

dy

Th

isB

eau

tifu

lL

ife

Sw

ing

53122352d

0e6

d9ec

bae9

435fe

ef218ce

Wit

hG

od

On

Ou

rS

ide

Bob

Dyla

nT

he

Tim

esT

hey

Ar.

..F

olk

7740d

47427258176a750d

edaf4

917ed

7

You

’re

AB

igG

irl

Now

Bob

Dyla

nB

lood

On

Th

eT

rack

sF

olk

db

b22e

1e5

3c3

d0c3

fb079158d

318ce

53

IW

ill

Pla

yM

yG

ame.

..B

ran

dN

ewD

eja

Ente

ndu

Pu

nk

16cb

b6959e8

7b

210aaae0

dc1

4cb

372a5

92A

vail

4A

MF

rid

ayP

un

k32e6

d44

9b

6f3

404353ab

bf0

fe40ea

8ef

72

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Dre

amer

Atm

osp

her

eW

hen

Lif

eG

ives

Y..

.H

ip-H

op

ab

4ad

954acc

06d

d92ec

564804f5

15b

40

Gu

ns

and

Cig

aret

tes

Atm

osp

her

eL

ucy

Ford

,T

he

At.

..H

ip-H

op

1105732d

cd789988a5117a566d

40ce

12

Blu

eB

eard

Ban

dO

fH

orse

sIn

fin

ite

Arm

sIn

die

8253fc

b5c6

c8afa

658d

795ce

2aa3f9

51

Bea

uti

ful

Atm

osp

her

eS

ad

Clo

wn

Bad

Win

...

Hip

-Hop

de1

7776

4d

819841485d

7a267a6d

76fd

b

Bre

akY

our

Fra

me

Bad

Ast

ron

au

tH

ou

ston

:W

eH

ave

...

Pu

nk

165ca

85e5

23b

6d

99d

1c0

dcf

cd1c6

b5e9

Bro

ken

Bad

Rel

igio

nT

he

Pro

cess

Of

Be.

..P

un

k5a49305d

c77593b

6298639f7

1c7

466b

1

Com

pli

cati

ons

Atm

osp

her

eO

verc

ast

!H

ip-H

op

60f8

670d

06ce

9079fa

1301e1

1ab

138d

3

Det

lef

Sch

rem

pf

Ban

dof

Hor

ses

Cea

seto

Beg

inIn

die

64a0b

2a2d

bc7

cfc4

64e9

619858d

09ae6

Evil

Sta

teA

ud

iocr

ush

So

You

Call

Th

ese.

..In

die

e28e8

c09628731d

e4e6

ab

9156c0

e6580

Fal

lA

par

tA

vail

Ove

rT

he

Jam

esP

un

ke7

afe

36e0

cdaa8c7

9b

121b

9871e0

740a

Fal

lIn

AR

iver

Bad

lyD

raw

nB

oyT

he

Hou

rO

fB

ewil

...

Ind

ie3b

b776c0

063a12fa

cba36d

521f7

f6d

af

AF

ighti

ng

Wor

dA

sT

all

As

Lio

ns

Blo

od

An

dA

ph

ori

sms

Ind

ieec

5f5

739504cc

b7338960d

90ee

6a1604

F**

*Y

ouL

ucy

Atm

osp

her

eG

od

Lov

esU

gly

Hip

-Hop

b38ce

4f338523fe

f01d

38694d

1acc

a62

Hap

pym

ess

Atm

osp

her

eS

ad

Clo

wn

Bad

Sp

r...

Hip

-Hop

d6315f5

96e3

5a35877211d

a2af7

d675e

Hea

rtA

Tac

tK

idD

yn

amit

e88

Fin

ger

sL

ou

ie..

.P

un

k02d

0ae6

3c0

9b

4b

43ee

4d

27ec

201b

bb

e8

Hel

itro

pe

At

Th

eD

rive

-In

VA

YA

Pu

nk

62455491d

75296f1

9299962331692590

Lev

eled

Ava

ilO

ne

Wre

nch

Pu

nk

da9cf

e1c3

e6c3

20894d

48b

80ab

5c0

c10

Liv

ing

Eac

hD

ayL

ik..

.A

trey

uS

uic

ide

Note

san

d..

.M

etal

f6f0

0856f7

3d

c5e5

5b

295ca

f004d

2826

Lov

e,L

ove,

Lov

e(L

...

As

Tal

lA

sL

ion

sA

sT

all

As

Lio

ns

Ind

ieb

cd4ad

e91ed

c3552273ed

dee

5b

c002c7

Moos

hB

igw

igA

nIn

vit

ati

on

To

...

Pu

nk

1835aeb

2af8

356092522508a25b

c080a

73

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Refl

ecti

ons

Atm

osp

her

eS

even

’sT

ravel

sH

ip-H

op

a5159d

f935ae3

43182161e2

49aaea

f33

Rel

ease

Th

eH

oun

ds

Ban

eG

ive

Blo

od

Pu

nk

eb763a1

bd

2b

2d

5b

1d

3811ff

b545028c9

Sle

epw

alk

Cap

sule

sA

tT

he

Dri

ve-I

nR

elati

on

ship

Of

C..

.P

un

k47f3

cb1034d

e8d

d003266d

e56a49ea

7d

Son

gA

vail

Dix

ieP

un

k1d

10965

f3939d

b1787fb

52f1

b58b

338f

Su

per

her

oB

ane

Hold

ing

Th

isM

om

ent

Pu

nk

25f9

1664ac7

0462eb

edca

5ef

60d

4f7

02

T.V

.A

uto

mat

ic7

Au

tom

ati

c7

Pu

nk

ba1254f

9b

ca069b

091e8

067b

059d

bd

e4

Th

eF

un

eral

Ban

dO

fH

orse

sE

very

thin

gA

llT

h..

.In

die

cf9501a0ad

9e1

383d

e5b

287855035413

Th

eP

aint

Ch

ips

Aw

ayB

ane

ItA

llC

om

esD

own

...

Pu

nk

6ef

72485a718363934b

e3b

b62a1b

3f7

8

Th

eyL

ook

edL

ike

St.

..B

aysi

de

Bay

sid

eP

un

k21a068d

56346acc

90a9d

b506b

5a3a63c

Tin

yV

oice

sB

adR

elig

ion

Str

an

ger

Th

an

Fic

...

Pu

nk

f3744d

82b

0f6

5ee

714a6a749999ea

067

Tom

orro

wB

adR

elig

ion

Gen

erato

rP

un

k58c0

1b

10c0

ace

dc9

988277f5

ad

7a043b

Unti

lW

eF

all

Au

dio

slav

eR

evel

ati

on

sA

lter

nati

veb

8e3

32d

f816a562a226a94f3

c44c8

40b

Wh

enB

adR

elig

ion

Su

ffer

Pu

nk

e212c1

9e1

6b

636b

3530cc

b8506194f5

6

Mat

tres

sA

tmos

ph

ere

Sad

Clo

wn

Bad

Su

m..

.H

ip-H

op

7824cd

0313681fb

b9f7

e1850053b

73b

f

Mu

sica

lC

hai

rsA

tmos

ph

ere

You

Can

’tIm

agin

e...

Hip

-Hop

064ce

062b

471782a15174e5

285ec

944e

Ju

stF

orS

how

Atm

osp

her

eT

he

Fam

ily

Sig

nH

ip-H

op

d30e4

62afa

6e8

6a0d

1078c9

650ce

f2ce

Blu

esfo

ra

Pla

yb

oyB

arn

eyK

esse

lM

usi

cT

oL

iste

nT

oJazz

4ef

2fd

901f4

7585804acf

67389b

6b

897

Eas

yL

ivin

gB

arn

eyK

esse

lP

oll

Win

ner

sT

hre

eJazz

25d

3702

32ef

e012cc

39b

63d

207258d

11

Go

Eas

y(S

eeth

eL

ove)

As

Tal

lA

sL

ion

sY

ou

Can

’tT

ake

It..

.In

die

910634fe

1260a6b

8c2

42710f5

e89f2

09

God

Dec

idin

gH

otW

ater

Mu

sic

Alk

ali

ne

Tri

o,

Ho..

.P

un

k1c9

46f5

49405993700a509982494e8

ed

74

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

How

Lon

gH

asT

his

B..

.B

arn

eyK

esse

lK

esse

lP

lays

Sta

n..

.Jazz

bee

971c

2290d

28c1

0ee

d91e9

3e8

90d

64

Mak

eS

omeo

ne

Hap

py

Bar

ney

Kes

sel

Poor

Bu

tter

fly

Jazz

dc9

75e9

e7f6

893842e2

f0a91a085d

f67

Tig

erR

agB

arn

eyK

esse

lL

et’s

Cook!

Jazz

8b

59781

0b

e5d

182429037cb

3a1b

45f1

e

Th

eB

est

Day

Atm

osp

her

eT

oA

llM

yF

rien

ds.

..H

ip-H

op

0c4

cc79112ed

3ae2

e02f5

854247d

eee1

AS

kel

eton

InT

he

C..

.A

nth

rax

Am

on

gT

he

Liv

ing

Met

al

04ad

80b

d5d

4c6

052e3

73ff

29693d

7c8

1

Acr

obat

As

Tal

lA

sL

ion

sL

afc

ad

ioIn

die

421ec

935f4

32b

1611a28b

3b

4f4

684fd

0

All

On

Bla

ckA

lkal

ine

Tri

oG

ood

Mou

rnin

gP

un

kcc

7d

7f4

4415297b

10aa5a49ee

df7

f0a8

An

oth

erIn

noce

nt

Gir

lA

lkal

ine

Tri

oF

rom

Her

eto

Infi

...

Pu

nk

cae0

ccd

cba8357c7

8745b

9fe

8b

3484b

d

As

You

Wer

eA

lkal

ine

Tri

oG

od

dam

nit

Pu

nk

68f5

a6eb

3a1d

a4d

a28ac4

263604d

6a16

Det

hb

edA

lkal

ine

Tri

oC

rim

son

Pu

nk

f8150e3

0180132ab

e4b

65eff

16a8c9

6b

Dow

nIn

AH

ole

Ali

ceIn

Ch

ain

sM

TV

Un

plu

gged

Alt

ern

ati

ve5ac8

81b

e5d

4912330716b

8f8

af9

61289

Fix

Ava

ilL

ive

at

the

Bott

o..

.P

un

k3f0

e62c7

79c0

8c8

aa5d

9d

fe712ac8

4d

d

Hea

dC

reep

sA

lice

InC

hai

ns

Ali

ceIn

Ch

ain

sA

lter

nati

ve5523767a343ab

9b

54c1

4e1

fc3f5

f3e9

1

IC

an’t

Rem

emb

erA

lice

InC

hai

ns

Face

lift

Alt

ern

ati

veb

7f6

728

e4ee

3d

8956d

9d

26258a1410b

1

My

Fri

end

Pet

erA

lkal

ine

Tri

oA

lkali

ne

Tri

oP

un

kc8

996ef

44e1

e8fb

fec6

259b

5ac0

5198f

Red

eye

All

Els

eF

ails

Th

eC

hase

Of

Gain

Pu

nk

674b

1fb

c821b

c09222ca

3b

343a7d

ac1

e

Sic

km

anA

lice

InC

hai

ns

Dir

tA

lter

nati

veb

d241aa1cf

34cb

dd

a16f5

8963694761e

Sle

epyh

ead

Alk

alin

eT

rio

May

be

I’ll

Catc

h..

.P

un

k63b

40ab

efff6

8c3

1837105395afc

d5b

3

Th

eA

mer

ican

Scr

eam

Alk

alin

eT

rio

Th

isA

dd

icti

on

Pu

nk

a2f4

6c3

2f7

f5d

374252a4f9

c5ee

8933a

Was

Th

ere?

All

anH

old

swort

hR

oad

Gam

esJazz

8d

670e2

0333c5

dcc

3b

161148ea

733acc

75

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Wh

ale

&W

asp

Ali

ceIn

Ch

ain

sJar

Of

Fli

esA

lter

nati

vea4a4022ca

1449ea

9322a55fc

375af3

3a

Wh

oC

ares

Win

sA

nth

rax

Sta

teO

fE

up

hori

aM

etal

9774b

942066508e8

80ab

2fc

e2c3

29306

Zon

eA

llan

Hol

dsw

ort

hA

llN

ight

Wro

ng

Jazz

35cd

7f2

95150f3

9110b

0b

134ed

a627b

f

Cla

vic

leA

lkal

ine

Tri

oD

am

nes

iaP

un

kfc

466622599b

562b

220f3

c8482c6

00f7

IF

oun

dA

way

Alk

alin

eT

rio

Agony

An

dIr

ony

Pu

nk

d6af0

9cd

7e0

a87835f1

f8c3

5ab

ce112f

Bor

ne

onth

eF

MW

av..

.A

gain

stM

e!N

ewW

ave

Pu

nk

97c0

f077ed

ab

a7eb

1a3a6555d

f2d

a5c3

Esp

irit

uA

lD

iM

eola

,P

aco

D..

.T

he

Gu

itar

Tri

oJazz

f7e7

6c8

e9aca

49e0

fb1e1

5005aac5

7c1

Gre

edy

All

Mass

Ner

der

Pu

nk

358a8f7

1c9

af2

cb4b

17f8

77f6

116a705

ItR

eall

yS

uck

sW

he.

..A

gain

stA

llA

uth

ori

tyD

estr

oyW

hat

Des

t...

Pu

nk

cbff

7407

7b

3851cf

20f6

f5b

3559656c0

Kev

inS

econ

ds

-E

xt.

..K

evin

Sec

ond

sS

pli

tP

un

k4894af2

1c7

7456964a0d

4602d

5640d

68

Ric

ean

dB

read

Aga

inst

Me!

As

Th

eE

tern

al

Co..

.P

un

k4b

55f1

2772e7

6f4

875f0

fcb

113d

58ac3

Sco

rch

Ale

xS

kol

nic

kT

rio

Tra

nsf

orm

ati

on

Jazz

9f1

0c7

2ce

52b

6e3

0429827f7

cbaf7

e9a

Stu

nn

ing

Up

onA

gain

stT

omorr

ow’s

Sky

Ju

mp

Th

eH

edges

F..

.In

die

e00d

22f

3f2

f93e6

7a7e2

2e3

62424972d

Th

eC

olle

geT

ryA

gain

stT

omorr

ow’s

Sky

Th

eL

ost

Tap

esIn

die

a49d

c3b

be6

36f0

fff0

31a267657e9

082

Jor

dan

’sF

irst

Ch

oice

Aga

inst

Me!

Rei

nve

nti

ng

Axl

Rose

Pu

nk

80b

a058

f53431f5

5d

7b

738b

e7d

f93249

Vio

len

ceA

gain

stM

e!S

earc

hin

gfo

ra

F..

.P

un

k547879aaa25494228ad

733d

69d

22eb

a0

Cer

eal

War

sA

FI

An

swer

Th

at

An

dS

...

Pu

nk

ea611937f3

6f1

6e9

bd

40762769fd

0419

Dea

thO

fS

easo

ns

AF

IS

ing

Th

eS

orr

owP

un

k56804d

635133aa0cd

61b

0fa

5a9a45fe

0

Ret

irin

gA

Wil

hel

mS

crea

mM

ute

Pri

nt

Pu

nk

8b

de5

e7c2

074fb

78021f0

ea728863929

Tob

yA

gain

stA

llA

uth

ori

tyA

llF

all

Dow

nP

un

k5fb

54c8

9b

18d

b6655c7

ed40d

165f6

c4d

76

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Bli

nk

88F

inge

rsL

ou

ieB

ehin

dB

ars

Pu

nk

0b

4459d

06085b

d900fd

122e7

996619d

a

Con

grat

ula

tion

sA

Wil

hel

mS

crea

mR

uin

erP

un

k18d

2f2

9d

b3f8

09f8

317ae2

d7d

05a41d

a

No

Poet

icD

evic

eA

FI

Bla

ckS

ail

sIn

Th

...

Pu

nk

22c9

b5a

f6c3

a741d

1f2

bd

ed37ad

14b

39

Th

isS

ecre

tN

inja

AF

IV

ery

Pro

ud

Of

Ya

Pu

nk

ca8138537b

ca4813fd

844d

143a7eb

6f2

Th

omas

AP

erfe

ctC

ircl

eM

erd

eN

om

sM

etal

93ce

1a6a4445ab

a32d

afe

e7f8

880d

d08

Ou

rG

hos

ts(c

onte

mp

...

AW

ilh

elm

Scr

eam

Care

erS

uic

ide

Pu

nk

1b

e791b

5928ad

47525707b

eddc9

88f5

e

F**

*T

his

Pla

ceF

righ

ten

edR

ab

bit

AF

righte

ned

Rab

b..

.In

die

a695f4

6f9

46e0

a155aaa7067b

d499f8

9

Aft

erT

he

Lau

ghte

r..

.An

dY

ouW

ill

Kn

o..

.S

ou

rce

Tags

&C

od

esIn

die

67004931ad

c13036cd

a4c2

fc3467e0

3a

Col

ourb

lin

d7

Sec

ond

sT

he

Cre

wP

un

k6fd

7189

f514b

a5a07ed

135ff

cab

197e9

Tak

eit

and

run

Dro

pkic

kM

urp

hys

Th

eW

arr

ior’

sC

od

eP

un

k74a5b

819555ea

cb9878b

38b

13a21af1

6

Bas

tard

sO

nP

arad

eD

rop

kic

kM

urp

hys

Bla

ckout

Pu

nk

b4210b

41b

82634d

61f1

1c1

a7f9

7261b

5

Sta

ind

Gla

ssA

nd

Ma.

..R

ise

Aga

inst

Un

rave

lin

gP

un

kca

08ce

5c3

559cd

1c4

a96b

45e4

fe92b

b5

Hig

hP

ress

ure

Low

Aga

inst

Me!

Wh

ite

Cro

sses

Pu

nk

a725d

cb1d

d072e8

f05d

8b

2215cc

41565

Ch

eek

To

Ch

eek

Geo

rge

Van

Ep

sL

egen

ds:

Solo

Gu

i...

Jazz

5d

59ad

03488f7

562528e1

222b

d758d

c9

Lie

toM

eD

epec

he

Mod

eS

om

eG

reat

Rew

ard

New

Wav

e6eb

0a73

a43d

8b

f14f9

91436421011633

Peo

ple

are

Peo

ple

(...

Dep

ech

eM

od

eS

om

eG

reat

Rew

ard

New

Wav

ee0

a60609fa

1838f0

95831b

296078ea

4c

See

You

(Liv

ein

Ha.

..D

epec

he

Mod

eA

Bro

ken

Fra

me

New

Wav

e6762e4

3e4

6f9

33d

a1e3

bd

d027d

1586f7

Sh

out

Dep

ech

eM

od

eS

pea

k&

Sp

ell

New

Wav

ec1

88f9

f1091d

99326264624074728fa

8

Wor

kH

ard

Dep

ech

eM

od

eC

on

stru

ctio

nT

ime.

..N

ewW

ave

a37b

7b

165911b

a2e1

883154327542fd

5

Cra

nk

(Liv

e)C

ath

erin

eW

hee

lW

ishvil

le(B

onu

s..

.A

lter

nati

veb

64a881

917298607e7

652c6

63d

7cc

667

77

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Wil

lin

gT

oW

ait

Cat

her

ine

Wh

eel

Ma

Soli

tud

a[D

isc

2]

Alt

ern

ati

vecff

0f8

a757cd

3190686c2

7cb

3cb

63aa7

Th

eR

oos

ter

Atm

osp

her

eS

ad

Clo

wn

Bad

Fal.

..H

ip-H

op

75b

1a29

de7

6e3

006d

c9b

9e2

3f1

7d

117a

All

IsF

ull

Of

Lov

eD

eath

Cab

for

Cu

tie

Th

eS

tab

ilit

yE

PIn

die

9c0

0d

7bb

aeb

662ae4

6a4695588e3

818f

Th

eT

hin

gsT

hat

Hat

...

Atm

osp

her

eS

tric

tly

Lea

kage

Hip

-Hop

e7ce

34aa696b

6ab

1d

17ce

7780e5

96417

Wh

ite

Noi

seA

tmos

ph

ere

Lea

kA

tW

ill

Hip

-Hop

8a6205b

97ee

03858a2b

ff46323d

ef534

Su

dd

enD

eath

InC

ar..

.B

ran

dN

ewY

ou

rF

avori

teW

ea..

.P

un

k4f8

eaca

782d

d7f1

0c5

b4e2

e246910b

29

Car

efu

lN

owC

opel

and

Eat,

Sle

ep,

Rep

eat

Ind

ie43d

4aa5

fc4b

82605161b

c9a9c7

0c8

10d

Sou

lM

eets

Bod

yD

eath

Cab

for

Cu

tie

Pla

ns

Ind

ie8ab

447d

2d

2894aeb

fd9ee

6e6

c7438b

1f

Sw

eete

stP

erfe

ctio

nD

epec

he

Mod

eV

iola

tor

New

Wav

e046f3

e9b

827c1

229c2

cb9633b

29f9

041

Nev

erB

ough

tIt

Din

osau

rJr

Han

dIt

Ove

rA

lter

nati

ve8f6

7d

7e6f4

672c5

0f1

b32d

e6c4

014ca

2

Fee

lT

he

Pai

nD

inos

aur

Jr

Wit

hou

ta

Soun

dA

lter

nati

ve0e8

b9d

f549afc

2c2

e221583459d

9776b

Sta

rtC

hop

pin

Din

osau

rJr.

Wh

ere

You

Bee

n?

Alt

ern

ati

vec9

b2972

a74e7

393c5

9554ce

43109d

8cb

Day

Tw

oE

xp

losi

ons

InT

he

Sky

Tra

vels

InC

on

sta..

.In

die

fe219b

7526409c4

bd

91630d

21014ad

75

You

An

dT

he

Nig

ht

A..

.Jam

ieC

ull

um

Poin

tles

sN

ost

alg

icJazz

44b

2b

4e1

00ea

c6e5

f2502e0

f65624fd

9

For

Jac

oJoh

nM

cLau

gh

lin

Ind

ust

rial

Zen

Jazz

eceb

075

22e9

2b

152fd

10a35b

9d

2692aa

Aft

erth

eF

act

Joh

nS

cofi

eld

Qu

iet

Jazz

d0ea

038

32f8

e3d

24343e3

c6222316d

13

(Beg

inn

ing

ofW

hat

’...

Kyu

ssW

retc

hS

ton

erM

etal

606689b

c8b

e37893d

5f0

58d

42322f0

af

100/

Sp

ace

Cad

et/D

em..

.K

yu

ssW

elco

me

toS

ky

Va..

.S

ton

erM

etal

981ec

afc

9fc

468804b

c5e5

5ea

e41e4

f5

50M

illi

onY

ear

Tri

...

Kyu

ssB

lues

for

the

Red

...

Sto

ner

Met

al

1601d

86145ee

de4

c5e5

e959d

0368b

456

AD

ayE

arly

and

aD

...

Kyu

ssM

uch

as

Gra

cias:

T..

.S

ton

erM

etal

cb944f8

0a2d

a9a24b

6b

290868464b

be2

78

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Bor

nto

Hu

laK

yu

ssQ

uee

ns

of

the

Sto

...

Sto

ner

Met

al

ca7fb

5bfd

2d

f8e2

dd

e7d

62d

39c4

d816b

Cat

amar

anK

yu

ss..

.An

dth

eC

ircu

s...

Sto

ner

Met

al

45f2

07ff

972ad

30ed

272468c4

d84a78b

Ab

ove

the

Tre

etop

sP

atM

eth

eny

Sec

ret

Sto

ryJazz

a284b

9a8ca

c86b

ce4ec

2b

9995048d

9d

d

Bra

inof

J.

Pea

rlJam

Yie

ldA

lter

nati

ve7977b

de2

149e5

1a70cd

a4b

aafc

4b

f318

Hey

You

Pin

kF

loyd

Th

eW

all

Rock

6ae0

8936b

bd

9695e9

95e7

1744b

290d

8a

InT

he

Fle

shP

ink

Flo

yd

Th

eW

all

Rock

014470cc

b0e6

f192d

1e5

44e4

0d

c708c5

1492

Cou

nti

ng

Cro

ws

Satu

rday

Nig

hts

&..

.A

lter

nati

vefc

eac5

6556090c5

96a1361d

8d

d97c3

db

Fol

low

Th

eR

eap

erC

hil

dre

nof

Bod

om

Foll

owT

he

Rea

per

...

Met

al

16ef

5a10094e4

81045f7

1c1

de1

c149ab

10.4

5A

mst

erd

amC

on..

.F

un

eral

For

AF

rien

dS

even

Way

sT

oS

cr..

.P

un

kd

bfa

97ff

88b

0a2374c8

e533ae6

32d

87e

45E

lvis

Cos

tell

oW

hen

IW

as

Cru

elR

ock

1659d

33909d

5ce

658b

ef5985f8

0d

9a27

6-00

Dre

amT

hea

ter

Aw

ake

Pro

g.

Met

al

07a74e0

3b

e2ef

8cf

acc

fdf5

f4e6

d44d

7

AP

ain

that

I’m

Use

...

Dep

ech

eM

od

eP

layin

gth

eA

ngel

New

Wav

e360b

59c

d45981c0

84959b

8488a0a4a2b

All

My

Lif

eF

oo

Fig

hte

rsO

ne

By

On

eA

lter

nati

ve92f1

3e3

a70a50324aab

c5488d

4856ed

a

Ap

oca

lyp

seN

owan

d..

.E

very

Tim

eI

Die

Gu

tter

Ph

enom

enon

Met

al

7d

b4eb

b50d

91ae9

299c4

c54b

dcc

f9f7

2

Arr

ows

Fir

ewor

ks

Gosp

elP

un

k0f4

ba52

3f3

eefc

aca

d1f1

701576e1

e67

Be

Cal

mF

un

.A

iman

dIg

nit

eIn

die

1641aa42c7

4ac8

c18fe

a983301369727

Ben

dto

Squ

ares

Dea

thC

abfo

rC

uti

eS

om

eth

ing

Ab

ou

tA

...

Ind

ied

472b

8b

81d

0702b

6fb

6ab

e76e2

90d

639

Bey

ond

Bel

ief

Elv

isC

oste

llo

Imp

eria

lB

edro

om

Rock

190ad

3131987729aa260ad

1d

8afd

d140

Bil

lO

fG

ood

sF

ace

To

Fac

eH

owT

oR

uin

Eve

ry..

.P

un

k17611d

31b

cf93e3

f8ca

a29f8

0364e6

cc

Blo

md

ido

Diz

zyG

ille

spie

Bir

k’s

Work

sJazz

c1d

d7d

a7ed

8e3

708085f7

8d

401211698

79

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Cat

apu

ltC

ounti

ng

Cro

ws

Rec

over

ing

Th

eS

a..

.A

lter

nati

vecf

6261c6

ac1

3d

e52d

dfa

7d

acf

eaf0

613

Tu

rnC

hri

stie

Fro

nt

Dri

ve

Anth

olo

gy

Ind

ie39111d

ea88c8

295ad

a614f5

37f1

878ee

Col

dA

sIc

eF

orei

gner

Th

eD

efin

itiv

eC

o..

.R

ock

7ff

0d

82b

8f7

01ee

05aca

bafd

c5e9

73b

e

Bri

ghte

stC

opel

and

Ben

eath

Med

icin

e..

.In

die

ab

73b

0ca

526452ce

950fd

1c6

229fc

347

Daa

hou

nd

Em

ily

Rem

ler

East

To

Wes

Jazz

d76199f

8f5

41b

40639a52902aac9

4e4

1

Bar

rel

ofa

Gu

nD

epec

he

Mod

eU

ltra

New

Wav

e4609d

255a061cd

a985482865282b

326b

Bla

ckC

eleb

rati

onD

epec

he

Mod

eB

lack

Cel

ebra

tion

New

Wav

ecf

c28ac9

8d

0b

64f3

7812a78ee

31d

8746

Dre

amO

nD

epec

he

Mod

eE

xci

ter

New

Wav

ed

68a98b

bf2

eb70b

1118a706d

e2a32960

IF

eel

You

Dep

ech

eM

od

eS

on

gs

of

Fait

han

...

New

Wav

e6f5

3ce

05b

702b

b615fb

c6a0ca

79ef

eb4

Dia

mon

dL

ight

Boog

ieC

her

ryP

opp

in’

Dad

die

sS

ou

ldC

ad

dy

Sw

ing

9a43165d

2a0d

ce52f6

ee53c1

f23f4

f5e

For

get

the

Sw

anD

inos

aur

Jr.

Din

osa

ur

Alt

ern

ati

vee1

db

c5c8

3aafc

7182a8969d

4d

6725c7

0

Fre

akS

cen

eD

inos

aur

Jr.

Bu

gA

lter

nati

vea6f9

cfc4

5b

4cd

a1423e2

6587d

78406d

0

ID

on’t

Th

ink

So

Din

osau

rJr.

ID

on

’tT

hin

kS

oA

lter

nati

ved

9c9

5f3

ef8d

1f4

3aed

19b

f25f6

aa6d

f3

Lit

tle

Fu

ryT

hin

gsD

inos

aur

Jr.

Foss

ils

Alt

ern

ati

vec1

47fa

8766494aa765508c6

78f7

a732e

Wh

atev

er’s

Cool

Wit

...

Din

osau

rJr.

Wh

ate

ver’

sC

ool

W..

.A

lter

nati

ved

56497e

19a237cf

35211fc

0980544a45

Dol

lars

An

dS

ense

Fad

edG

rey

AQ

uie

tT

ime

Of

D..

.P

un

k58e6

137d

65d

5a1fa

b96f7

70e9

a3cf

4a0

Fam

ous

For

Not

hin

gD

rop

kic

kM

urp

hys

Th

eM

ean

est

Of

Ti.

..P

un

k60b

0492

f3d

5f9

f05fa

5b

28c6

0821ed

a9

Dru

nk

Dad

dy

Ch

erry

Pop

pin

’D

ad

die

sF

eroci

ousl

yS

ton

edS

win

g20d

4237

4955509b

0d

f8575648855d

de9

Fir

stB

reat

hA

fter

...

Exp

losi

ons

InT

he

Sky

Th

eE

art

hIs

Not

...

Ind

ie8f2

4b

7343ab

be5

0809310d

7d

3554ec

71

Th

eB

irth

and

the

D..

.E

xp

losi

ons

InT

he

Sky

All

of

aS

ud

den

I...

Ind

ie9191b

37e3

570c4

6fc

115986264781570

80

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

You

’ve

Don

eN

othin

gF

ace

To

Fac

eD

on

’tT

urn

Aw

ayP

un

ke8

26f8

49237e4

cb4a5951c8

e9ef

7c0

2c

Fam

ily

Syst

emC

hev

elle

Won

der

Wh

at’

sN

ext

Met

al

5508ec

d353ab

ffacc

dc6

10d

c97cc

f686

Fro

mF

inis

hto

Sta

r...

Cou

nte

rfit

Fro

mF

inis

hto

St.

..In

die

d68d

290fb

76d

f0c9

408b

51d

e63b

cc655

Fro

mW

est

Tex

asE

xp

losi

ons

InT

he

Sky

Fri

day

Nig

ht

Lig

hts

Ind

iea5513ca

c22d

a1712d

41942c0

67b

a5572

Th

eM

oon

IsD

own

Fu

rth

erS

eem

sF

ore

ver

Hop

eT

his

Fin

ds

Y..

.In

die

a62b

faf4

df9

bad

9a4810ace

93373111f

Good

Bai

tD

izzy

Gil

lesp

ieD

izzy

Atm

osp

her

eJazz

33c9

a8b

a09e3

782835fa

e910ed

127d

3e

Gre

etD

eath

Exp

losi

ons

InT

he

Sky

Th

ose

Wh

oT

ell

Th

...

Ind

ieb

61b

7b

2fe

de5

78b

05b

b823d

a36309d

94

Gro

ovin

’H

igh

Ch

arli

eP

arker

Yard

bir

dS

uit

eD

i...

Jazz

b2a3c4

3ec

25314fd

6f7

bf7

5e3

bb

19807

Han

gin

arou

nd

Cou

nti

ng

Cro

ws

Th

isD

eser

tL

ife

Alt

ern

ati

veb

1549e4

db

b6f6

f1a24b

24f8

7a326ff

7a

Har

dC

and

yC

ounti

ng

Cro

ws

Hard

Can

dy

Alt

ern

ati

vee5

d812b

346c9

ac2

4a729075d

a9d

e9ce

b

Hea

ven’s

Not

Ove

rfl..

.C

orro

sion

Of

Con

for.

..D

eliv

eran

ceS

ton

erM

etal

4b

7b

1152b

53057d

54fd

0c2

55d

ad

01cc

1

Hol

din

gS

omeo

ne’

sH

...

Cir

caS

urv

ive

Ju

turn

aIn

die

9b

71f8

8886664f3

faff

882b

83ac5

c034

Hom

eIs

aF

ire

Dea

thC

abfo

rC

uti

eC

od

esan

dK

eys

Ind

ie30f5

f4d

a9a3c6

4b

dab

16d

b103e9

22f9

b

How

To

Sta

rtA

Fir

eF

urt

her

See

ms

Fore

ver

How

To

Sta

rtA

Fir

eIn

die

fa90934a29eb

6c5

3424601339d

14b

e49

IF

eel

Fre

eC

ream

Th

eB

est

Of

Cre

am

...

Rock

c06e0

2ef

0ae9

cb56a18e2

8f7

2d

30c9

c8

IfT

hey

Mov

e...

Kil

l...

Fai

rwea

ther

IfT

hey

Mov

e...

Ki.

..P

un

kef

0379d

31ed

555a440c3

522c4

2f9

d572

Imm

ure

Dam

iera

MU

SIC

Ind

iee1

343d

447d

98cc

53b

2eb

58aec

bfa

cf0a

Ina

Sen

tim

enta

lM

ood

Du

keE

llin

gton

Bes

tof

Du

keE

lli.

..Jazz

e7d

1f3

e241250ef

4b

35158e7

6a1c0

b98

InC

hai

ns

Dep

ech

eM

od

eS

ou

nd

sO

fT

he

Un

i...

New

Wav

e56e0

cf883a20203f7

1c3

5cd

cacf

4b

9c8

Invo

cati

onO

fA

poca

...

Dra

gon

For

ceV

all

eyO

fT

he

Dam

ned

Met

al

6d

6a3d

7315f7

dd

2b

455c8

ea53c9

6ef

5b

81

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Thu

mb

JM

asci

sM

art

in+

Me

Alt

ern

ati

ve2d

d6c6

456064d

49e2

db

90cf

5ce

d01d

67

Kee

pin

gth

eB

lad

eC

ohee

dan

dC

am

bri

aG

ood

Ap

oll

o,

I’m

...

Pu

nk

e5ea

ec7e3

33e8

9c3

20902b

92fb

a3a165

Kid

sO

nT

he

Str

eet

Ch

erry

Pop

pin

’D

ad

die

sK

ids

on

the

Str

eet

Sw

ing

ae4

a7b

c4f4

971360246d

ad

3ae0

01fe

1b

Kin

gof

the

Rot

ten

Cor

rosi

onO

fC

on

for.

..W

iseb

lood

Sto

ner

Met

al

522acf

2e8

dc0

fc472b

f694d

ac3

e317b

b

Kis

sO

fD

eath

Dok

ken

Back

For

Th

eA

ttack

Met

al

30aee

34b

6e5

1158a917ce

d251195d

9f7

Lig

ht

Up

Ah

ead

Fu

rth

erS

eem

sF

ore

ver

Hid

eN

oth

ing

Ind

ied

5f1

b746e3

43b

715f5

0989083c2

6681a

Liv

ing

Dea

dB

eat

Ch

ild

ren

ofB

od

om

Are

You

Dea

dY

et?

Met

al

9b

cbb

0762fd

ab

349e3

1aff

3213e7

f049

My

Sp

irit

Wil

lG

oO

nD

rago

nF

orce

Son

icF

ires

torm

Met

al

bd

9399f4

063278b

2025e6

f6b

73ae7

bc5

Nee

dle

d24

,7C

hil

dre

nof

Bod

om

Hate

Cre

wD

eath

roll

Met

al

06b

5ef

31d

23d

148682c4

1b

41d

0e4

4d

99

No

Act

ion

Elv

isC

oste

llo

Th

isY

ears

Mod

elR

ock

9a178e5

7651f8

a1577f9

f8d

25a1ad

95d

No

On

eR

eall

yW

ins

Cop

elan

dIn

Moti

on

Ind

ie2ee

d6b

d4a1643291d

57086a8f4

f7960a

On

eE

yed

ea&

Ab

ilit

ies

Fir

stB

orn

Hip

-Hop

2287cd

13f0

90b

b53f9

9eb

071b

fac4

9e2

Op

enU

pan

dW

ail

Chu

ckR

agan

Los

Fel

izIn

die

01e1

c728ff

7a73a22e1

635a38153ec

2a

Oro

,In

cien

soY

Mir

raD

izzy

Gil

lesp

ieA

fro-C

ub

an

Jazz

M..

.Jazz

68e5

ad

778b

4b

5c1

22b

36e6

9d

4b

825018

Ou

rD

elig

ht

Diz

zyG

ille

spie

Cool

Bre

eze

Jazz

48d

0846

3e4

ff1e2

c71d

a7c5

e547e2

421

Per

did

oD

izzy

Gil

lesp

ieS

alt

Pea

nu

tsJazz

e906b

38e1

36711261e7

a475442b

77b

df

Pol

itik

Col

dp

lay

AR

ush

Of

Blo

od

T..

.In

die

5094d

9afd

e5b

aef

58b

06574f5

e8ca

e81

Pre

lud

eC

hio

dos

All

’sW

ell

Th

at

E..

.P

un

k46f7

76d

66344ff

26066b

18f1

6573e2

83

Pu

llM

eU

nder

Dre

amT

hea

ter

Images

And

Word

sP

rog.

Met

al

72a0d

7f72d

6c1

f66a1b

9e9

1f6

aef

ceb

d

Reg

ress

ion

Dre

amT

hea

ter

Met

rop

oli

sP

t.2

S..

.P

rog.

Met

al

2e4

813801b

fee3

4cb

944606b

e0c2

5ac3

82

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Rom

eoA

Go-

Go

Eve

ryT

ime

ID

ieH

ot

Dam

n!

Met

al

b6413d

577e8

f7d

241a0cc

08b

c096b

e29

Rou

nd

Her

eC

ounti

ng

Cro

ws

Au

gu

stan

dE

very

t...

Alt

ern

ati

ve886898a1389b

47b

fb2b

fb740d

7d

557c7

Sch

ool

dD

ays

Diz

zyG

ille

spie

Imp

rom

ptu

Jazz

a7322b

e4e6

1c7

a274558af9

5e5

3fc

ff7

Scr

app

leF

rom

the

A..

.C

har

lie

Par

ker

Yard

bir

dS

uit

eD

i...

Jazz

f7c0

d45

0774e0

7d

228ce

082f9

f50b

8e8

Sig

ne

Eri

cC

lap

ton

Un

plu

gged

Blu

es454836e1

32d

51901f1

858aab

67ca

f2c8

Slu

nky

(Ori

gin

alm

ix)

Eri

cC

lap

ton

Eri

cC

lap

ton

Del

u..

.B

lues

06cc

086a1640b

454d

e1ad

ae7

deb

41508

Slu

nky

Eri

cC

lap

ton

Eri

cC

lap

ton

Del

u..

.B

lues

bd

80cd

726d

bc0

7f6

fdcb

2e6

34a46c8

10

Som

eN

ights

Intr

oF

un

.S

om

eN

ights

Ind

ie718067f3

ef0fd

f5a51d

565159b

cad

ef9

Sou

ven

irC

ounte

rfit

Su

per

Am

use

men

tM

...

Ind

ied

34fb

07ff

47277e9

46160f5

3684cb

141

Sow

ing

Sea

son

(Yea

h)

Bra

nd

New

Th

eD

evil

An

dG

od

...

Pu

nk

f300251868f7

837d

5ab

b6eb

ffa947502

Ste

adie

rF

oot

ing

Dea

thC

abfo

rC

uti

eT

he

Ph

oto

Alb

um

Ind

ie92f2

f19f4

014c8

d929840b

75ed

9738e9

Sto

ne

Bre

aker

Cor

rosi

onO

fC

on

for.

..In

Th

eA

rms

Of

God

Sto

ner

Met

al

6d

4cd

c6b

183e3

c954f3

63b

186a1fe

18c

Tal

kin

gD

esce

nd

ents

Cool

To

Be

You

Pu

nk

26ac6

c18ee

4b

5e2

40a26e5

228e3

f7150

Th

eB

rill

iant

Dan

ceD

ashb

oard

Con

fess

ion

al

Th

eP

lace

sY

ou

Ha..

.In

die

5128881b

9431f0

92e4

ee4ed

01b

e5828d

Th

eG

reys

Fri

ghte

ned

Rab

bit

Sin

gT

he

Gre

ys

Ind

ie1fb

a10c

e024d

0e2

c5a074eb

0754f2

183

Th

eM

od

ern

Lep

erF

righ

ten

edR

ab

bit

Th

eM

idn

ight

Org

a..

.In

die

077369139c2

1a6af6

81b

097b

423ef

eed

Th

eM

oon

IsD

own

Fu

rth

erS

eem

sF

ore

ver

Th

eM

oon

IsD

own

Ind

ie4a9280f6

d8a01603b

5c8

4e5

d524e9

a60

Th

eN

ewY

ear

Dea

thC

abfo

rC

uti

eT

ran

satl

anti

cism

Ind

ie3a8fb

f6436f9

68f0

35f0

ee90e4

19ae4

a

Th

eR

oot

Of

All

Evil

Dre

amT

hea

ter

Oct

avari

um

Pro

g.

Met

al

df0

a451

a0084f4

809c4

59d

1107c4

c15a

Th

eS

earc

hC

her

ryP

opp

in’

Dad

die

sR

ap

idC

ity

Mu

scle

...

Sw

ing

b55b

5635b

09ae6

439fc

2b

1c6

b4c9

ef3e

83

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Th

ese

Sh

rou

ded

Tem

ple

sC

orro

sion

Of

Con

for.

..B

lin

dS

ton

erM

etal

a7026c1

2eb

3d

0173a55d

b92fa

858ca

63

Th

ings

Fri

ghte

ned

Rab

bit

Th

eW

inte

rof

Mix

...

Ind

ie37465ca

905d

7671e7

6d

31451b

63b

a58d

Tit

leT

rack

Dea

thC

abfo

rC

uti

eW

eH

ave

Th

eF

act

s...

Ind

ied

160106

865773e4

4574d

db

16110d

87b

3

Un

chai

nT

he

Nig

ht

Dok

ken

Un

der

Lock

An

dK

eyM

etal

f23a32d

902263866e2

c65b

38eb

18ff

2a

Wom

enD

efL

epp

ard

Hyst

eria

Met

al

683ef

59f4

eea6c1

fe5f5

1a7883c8

fb12

Zoot

Su

itR

iot

Ch

erry

Pop

pin

’D

ad

die

sZ

oot

Su

itR

iot

Sw

ing

d5733d

e00ca

4e2

9f9

ee229d

1e3

be3

368

AS

ong

For

Ou

rF

ath

ers

Exp

losi

ons

InT

he

Sky

How

Str

an

ge,

Inn

o..

.In

die

8a137d

90ca

daf2

658a56ef

956cc

8a69d

Act

sof

Man

Fri

ghte

ned

Rab

bit

Ped

estr

ian

Ver

seIn

die

5e4

667e8

60999cd

ab

2f6

4aee

789b

69f3

Bad

Boy

frie

nd

Gar

bag

eB

leed

Lik

eM

eA

lter

nati

veb

82ad

fa29b

c7a84a2719a00133ab

48c1

Bix

by

Can

yon

Bri

dge

Dea

thC

abfo

rC

uti

eN

arr

owS

tair

sIn

die

f724938a630a215093a40b

3f8

a5680b

5

Lit

tle

Bri

bes

Dea

thC

abfo

rC

uti

eT

he

Op

enD

oor

EP

Ind

iec2

5a153e2

437d

bb

50fe

3f8

dd

67c0

168e

Nev

erL

etM

eD

own

A..

.D

epec

he

Mod

eM

usi

cF

or

Th

eM

ass

esN

ewW

ave

4e5

4c9

0370b

5b

5015b

8267d

c4223a10f

Rei

ntr

od

uci

ng

Eye

dea

&A

bil

itie

sE

&A

Hip

-Hop

456ab

bf0

b03b

d2375766b

c8e1

1792661

Hay

Fev

erE

yed

ea&

Ab

ilit

ies

By

Th

eT

hro

at

Hip

-Hop

4fc

3d

7afe

bd

9d

bb

a8c2

48665524e9

860

Han

g’E

mH

igh

Dro

pkic

kM

urp

hys

Goin

gO

ut

InS

tyle

Pu

nk

b980c5

15eb

fcb

db

28cc

37fa

050d

f0b

c1

Hel

lhou

nd

sO

nM

yT

rail

Ch

ild

ren

ofB

od

om

Blo

od

dru

nk

Met

al

593afd

cc7a44821d

16c5

7ca

a92a2224d

Las

tK

now

nS

urr

oun

d..

.E

xp

losi

ons

InT

he

Sky

Take

Care

,T

ake

C..

.In

die

72c0

528f9

238b

82e5

365e0

8254806627

Lif

eIn

Tec

hn

icol

orC

old

pla

yV

iva

La

Vid

aIn

die

ae8

a4ef

251c8

f84f3

9013038ee

0144a1

Lov

e,in

Itse

lfD

epec

he

Mod

eC

on

stru

ctio

nT

ime.

..N

ewW

ave

65c6

79ed

e1555a10aaaaed

79034a9fd

1

My

Sec

ret

Gar

den

Dep

ech

eM

od

eA

Bro

ken

Fra

me

New

Wav

e66a436ab

e24e9

d4b

a785e5

983a4f5

df7

84

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

New

Lif

eD

epec

he

Mod

eS

pea

k&

Sp

ell

New

Wav

e95c4

b3b

2b

6eb

7506d

2e3

5076d

65b

92cc

Sat

urd

ayC

hri

stie

Fro

nt

Dri

ve

Ch

rist

ieF

ront

Dri

ve

Ind

ie046b

623

ae6

af0

def

41455ce

be7

d3b

1e5

Sh

ould

Anyth

ing

Go

...

Fac

eT

oF

ace

Lau

gh

Now

...L

au

gh

...

Pu

nk

fa66ff

2a07487b

bcb

8e9

1d

d25e2

82f9

7

Shu

tY

our

Mou

thG

arb

age

bea

uti

fulg

arb

age

Alt

ern

ati

vec1

8486afe

f940563089490d

287c2

9b

0f

Unti

tled

(Lov

eS

ong)

Cou

nti

ng

Cro

ws

Un

der

wate

rS

un

shin

eA

lter

nati

vef0

067d

e21686b

4511d

c66826625ef

3e4

Way

dow

nC

ath

erin

eW

hee

lW

ayd

own

[1]

Alt

ern

ati

ve24cf

7ce

ae0

821c7

16e9

291c5

d0f7

f704

We

Lau

ghIn

door

s(T

...

Dea

thC

abfo

rC

uti

eT

he

Joh

nB

yrd

E.P

.In

die

6c2

3e0

851d

fc46a9d

728d

2b

efb

72b

298

Mylo

Xylo

toC

old

pla

yM

ylo

Xylo

toIn

die

e1110ce

86c0

a5f6

5d

2d

9d

ea2c2

a60670

Sh

ould

You

Ret

urn

Cop

elan

dY

ou

Are

My

Su

nsh

ine

Ind

ied

e0afe

cdc1

dee

a07c4

3649e4

68366d

76

Daa

hou

dE

mil

yR

emle

rR

etro

spec

tive

Vol.

..Jazz

035172b

dca

326d

b82c1

ab

bc1

52c6

e2c5

Ph

otob

oot

hD

eath

Cab

for

Cu

tie

Forb

idd

enL

ove

E.P

.In

die

b69c2

9d6b

9b

a7f0

ec803fa

e7992049c5

Ele

ctra

Mad

eM

eB

lin

dE

verc

lear

Sp

ark

lean

dF

ad

eA

lter

nati

vefd

0844d

82c0

da9d

b567282f7

95732129

Dis

app

ointe

dF

ace

To

Fac

eR

eact

ion

ary

Pu

nk

2f4

9d

6db

f02ace

6873466cb

8a47a241f

Ove

rcom

eF

ace

To

Fac

eIg

nora

nce

isB

liss

Pu

nk

f845b

bf2

9b

e5c1

e321812b

68825a7c1

7

Res

ign

atio

nF

ace

To

Fac

eF

ace

To

Face

Pu

nk

a2084b

725a58fc

da340af3

42b

383e7

c8

Str

ugg

leF

ace

To

Fac

eB

igC

hoic

eP

un

kb

4fa

c71f3

1d

df6

96ad

2aca

43ed

1789b

7

Wal

kT

he

Wal

kF

ace

To

Fac

eL

ive

Pu

nk

66d

07f7

44c0

d743b

3b

77fb

c503a441ad

123

Dro

pF

ace

To

Fac

eT

hre

eC

hord

sA

nd

...

Pu

nk

511c2

eab

7e9

63a111b

0e4

287c2

64a3a0

Geo

grap

hy,

Von

neg

ut.

..F

irew

orks

All

IH

ave

To

Off

...

Pu

nk

31b

2586

4d

b5e1

887c9

96957723ce

b8ae

Hel

ena

Bea

tF

oste

rT

he

Peo

ple

Torc

hes

Ind

ief0

6452b

3e2

9336a3964e2

24b

f88a2f0

3

85

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

So

Col

dF

urt

her

See

ms

Fore

ver

Pen

ny

Bla

ckIn

die

33d

a9e7

63f7

7ed

ff5fd

f863099fa

2621

IG

otP

lenty

o’N

utt

inE

lla

Fit

zger

ald

an

d...

Ell

a&

Lou

isS

ing..

.Jazz

3c9

919393ee

96f4

965650ad

350b

1d

14d

Ingr

edia

nts

Con

stel

lati

onof

Cars

Con

stel

lati

on

of

...

Ind

ieb

9e5

9d

7e1

523fd

83ae4

6a34487fa

a3d

e

Let

’sC

all

Th

eW

hol

...

Ell

aF

itzg

erald

an

d...

Th

eB

est

Of

Ell

a..

.Jazz

f88299b

27722e6

c33961210d

736f6

5cf

Lim

ehou

seB

lues

Dja

ngo

Rei

nh

ard

tT

he

Bes

tof

Dja

ng..

.Jazz

9fe

ab

b3d

f172cc

f7fc

bca

1507ab

6fd

02

Moch

aS

pic

eE

mil

yR

emle

rR

etro

spec

tive

Vol.

..Jazz

c3b

1d

cd0798e7

d4b

bca

f14e2

4aa50d

24

My

Mel

anch

oly

Bab

yD

izzy

Gil

lesp

ieJazz

Coll

ecto

rE

d..

.Jazz

d6f4

f32fb

5fe

236e0

e527a8486d

9ea

58

Sev

enC

ome

Ele

ven

Ch

arli

eC

hri

stia

nT

he

Ori

gin

al

Gu

it..

.Jazz

14d

523e

f6c3

bf1

328543d

6525ed

2d

a54

So

Mu

chfo

rth

eA

ft..

.E

verc

lear

So

Mu

chfo

rth

eA

...

Alt

ern

ati

ve01b

0d

2564c5

5fe

9d

31e9

a1eb

551aff

b4

Son

gF

rom

anA

mer

ic..

.E

verc

lear

Son

gs

Fro

man

Am

e...

Alt

ern

ati

ve47e0

7a7b

faf8

478fb

02b

ac8

2c5

a0a3c2

Sp

arks

Are

Gon

na

Fly

Cat

her

ine

Wh

eel

Wis

hvil

leA

lter

nati

ve18d

5eb

152c6

2d

67c2

792c1

64386a0405

Su

per

vix

enG

arb

age

Garb

age

Alt

ern

ati

vefe

c9605433fa

2f5

15e0

3a92fc

db

76668

Th

eR

eap

ing

Coh

eed

and

Cam

bri

aG

ood

Ap

oll

oI’

mB

...

Pu

nk

7a3f9

ebf5

eb0ca

4e5

74a32d

6e0

792d

62

Th

eR

ing

inR

etu

rnC

ohee

dan

dC

am

bri

aIn

Kee

pin

gS

ecre

t...

Pu

nk

d1b

9b

40a8c2

c61e0

0b

9ff

d3f0

9d

1f0

4e

You

Mu

stB

eL

osin

g..

.F

ats

Wal

ler

Port

rait

Vol.

2-

...

Jazz

ae1

b4b

92e0

9d

3570043ee

70285c7

db

86

You

rG

eniu

sH

and

sE

verc

lear

Worl

dO

fN

ois

eA

lter

nati

ve5a8e8

9d

901ed

723e9

bafb

12d

961f0

87f

Squ

are

On

eC

old

pla

yX

&Y

Ind

ieb

ca69d

1e5

03a266b

e0d

ff0aad

0d

dd244

Not

My

Fu

ner

alC

hil

dre

nof

Bod

om

Rel

entl

ess,

Rec

kl.

..M

etal

ab

ba7b

0cc

2804f7

5550ea

343197fb

752

Bed

Bra

nd

New

Dais

yP

un

k29090ef

eac1

ab

3ab

4ab

39562fd

7b

836b

Bes

tof

Me

Big

wig

Un

-Mer

ryM

elod

ies

Pu

nk

11664ce

ded

60fb

bf2

57b

fa787155003d

86

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Dir

tR

oad

Blu

esB

obD

yla

nT

ime

Ou

tO

fM

ind

Folk

3fe

a99180d

86004ae1

b65264816f1

bd

6

Cap

rico

rnB

raid

Fra

nkie

Wel

fare

B..

.In

die

12e6

2d

edaa1277fd

6b

c2c7

678d

54a3a4

Jim

my

Go

Sw

imm

erB

raid

Mov

ieM

usi

cvo

l.T

wo

Ind

ie94e6

bf3

e0148fa

f11e4

17b

5d

72100e8

d

Cou

ntr

yH

ouse

Blu

rT

he

Gre

at

Esc

ap

eA

lter

nati

ve74316b

f48a634751c8

a48b

b4b

d3e7

cb7

Day

ligh

tB

ette

rT

han

Ezr

aB

efore

Th

eR

ob

ots

Alt

ern

ati

ve19d

38b

c9ce

464e4

94d

46f5

190001f8

67

Dec

kT

he

Hal

lsB

ing

Cro

sby

Fra

nk

...

Ch

rist

mas

Jazz

08f9

3cd

3d

bfa

7f4

bab

7023d

9cb

bf2

49d

Den

tB

igw

igS

tay

Asl

eep

Pu

nk

cfb

b431d

fed

942ea

47802d

24ae2

017d

a

Kra

cked

Din

osau

rJr.

You

’re

Liv

ing

All

...

Alt

ern

ati

vef4

b04a9

69ca

e0456ed

613e7

49eff

b1fb

Don

’tC

han

geY

our

P..

.B

enF

old

sF

ive

Th

eU

nau

thori

zed

...

Ind

ie0c2

77b

6498f9

129e8

492e6

8168e4

61ae

Fai

rB

enF

old

sF

ive

Wh

ate

ver

An

dE

ver.

..In

die

cbaf3

01821d

15c1

c68c8

480554141ec

8

Fel

icia

Blu

esT

ravel

erS

traig

ht

on

Til

l..

.A

lter

nati

ve6e8

a17042e4

8d

3b

ae6

98343ea

27137cc

Fu

ture

Boy

Cat

her

ine

Wh

eel

Ad

am

An

dE

ve-

Me.

..A

lter

nati

vece

377ca

5b

ef874d

e53f4

08632ed

5d

10d

Good

Bet

ter

Th

anE

zra

Del

uxe

Alt

ern

ati

ve7899ed

cf51b

5ce

7b

003b

e4d

13b

f10532

Hig

her

Pow

erB

osto

nG

reate

stH

its

Rock

4488cb

3b

572f9

4039a186d

45c2

83cd

ee

IC

onfe

ssC

ath

erin

eW

hee

lC

hro

me

Alt

ern

ati

vee1

77893b

b9a483f0

565b

a0852b

1f5

1fe

IW

ant

To

Tou

chY

ouC

ath

erin

eW

hee

lF

erm

ent

Alt

ern

ati

veb

c5b

7c0

cafa

596b

b5b

900ea

2593e9

dc2

Lon

ely

Man

Bil

lF

rise

llD

isfa

rmer

(Sta

nd

ard

)Jazz

18a880d

fc2984d

5d

8047ce

0d

3c7

78af2

Lov

e&

Gre

edB

lues

Tra

vel

erS

ave

His

Sou

lA

lter

nati

ve5c8

cbd

014c3

216f0

42e1

d5e9

47ee

5214

Mon

key

Wre

nch

Foo

Fig

hte

rsC

olo

ur

&T

he

Sh

ap

eA

lter

nati

ve9b

e9f0

17a186d

d574a3b

754ec

86f7

f67

Mot

ion

Lig

ht

Bra

idM

ovie

Mu

sic

Vol.

On

eIn

die

bf6

090b

6cf

69155d

609762d

bd

1539d

6a

87

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Old

Tim

esB

ill

Fri

sell

and

85..

.S

ign

Of

Lif

eJazz

f6369a74e7

3f7

9666d

fc7887f3

ab

9705

Qu

een

Of

Sor

row

Bla

ckL

abel

Soci

ety

Han

gov

erM

usi

cV

o..

.M

etal

9b

bf8

c5e2

dd

0b

cccf

4ee

461d

90e5

d84a

Rew

ind

Bet

ter

Th

anE

zra

Fri

ctio

n,

Baby

Alt

ern

ati

vea4e7

d24

51493fc

41262a01afb

28b

0e1

6

Son

g2

Blu

rB

lur

Alt

ern

ati

ve33ca

9cc

3b

897611d

5789e7

23b

0f5

9725

Sta

nd

Blu

esT

ravel

erF

ou

rA

lter

nati

ve9e1

9cf

2fd

3ad

3e8

67a126c8

837f3

c809

Sw

imB

ush

Six

teen

Sto

ne

Alt

ern

ati

vefa

f033c9

395a268348e1

f1b

1ad

3d

54b

3

take

ach

ance

onlo

veB

rian

Set

zer

13

Rock

abil

ly29ab

dc9

f2889c6

9fb

f672d

76cd

5cb

9c8

Th

eN

ewS

tyle

Bea

stie

Boy

sL

icen

sed

To

Ill

Hip

-Hop

ba6f7

75d

dcb

0d

a7e6

7b

e1a4f0

1b

6c0

8b

Th

eR

ose

Pet

alle

dG

...

Bla

ckL

abel

Soci

ety

Son

icB

rew

Met

al

1875ff

8c5

c03601d

7d

2049b

2a6a203f4

Tou

ghG

uy

Bea

stie

Boy

sIl

lC

om

mu

nic

ati

on

Hip

-Hop

a4d

c9f7

b5219aa5e5

74d

0e7

f5c6

b7ce

1

Way

dow

nC

ath

erin

eW

hee

lH

ap

py

Day

sA

lter

nati

ve13ae2

6883ab

12a4d

2f5

37e6

dd

3363190

Wh

at’s

InY

ouB

lack

Lab

elS

oci

ety

Mafi

aM

etal

b3e2

95d

2178b

695e8

9d

15ee

127fc

20eb

Wh

ere

My

For

tun

eL

ies

Cac

oph

ony

Sp

eed

Met

al

Sym

ph

ony

Met

al

774b

f6669892c8

70e7

ea0011c6

52b

dcd

Cra

nk

(Liv

e)C

ath

erin

eW

hee

lB

roke

nN

ose

[Dis

c1]

Alt

ern

ati

ve37b

da82a2b

808f6

90f8

052c8

8d

498c2

f

Del

icio

us

Cat

her

ine

Wh

eel

Ma

Soli

tud

a[D

isc

1]

Alt

ern

ati

vec3

4d

a38

4e6

9fb

0e7

4822231b

f8c7

07cc

Flo

wer

To

Hid

e(L

iv..

.C

ath

erin

eW

hee

lB

roke

nN

ose

[Dis

c2]

Alt

ern

ati

vee5

ae7

2d

82b

eb65504a9ed

6670d

0fb

cd8

Gir

lF

rom

Th

eN

orth

...

Bob

Dyla

nT

he

Fre

ewh

eeli

n’

...

Folk

d2c7

a66

a637809a192c9

fa4b

3046c3

88

God

Insi

de

My

Hea

dC

ath

erin

eW

hee

lJu

dy

Sta

rin

gA

tT

...

Alt

ern

ati

veb

844139

5999a057d

561b

f5332f4

54605

Kil

lin

gA

Cam

era

Bra

idF

ram

e&

Canva

sIn

die

7f7

301b

d8c5

cc0ad

720b

561ad

84c0

057

Log

anT

oG

over

nm

ent.

..B

ran

dN

ewT

he

Holi

day

EP

Pu

nk

5d

a68c8

04775aa41c5

4d

2e4

a607e5

4e7

88

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Nin

etee

n75

Bra

idT

he

Age

of

Oct

een

Ind

ie33252b

29264ad

52ed

752b

a888f9

1790b

Ple

asu

reC

ath

erin

eW

hee

lC

ran

kA

lter

nati

ve542a82676d

c2ea

0b

3e7

954cb

3e7

86f9

2

Th

eN

ewN

ath

anD

etr.

..B

raid

Lu

cky

To

Be

Ali

veIn

die

1f3

a9d

c3a53974176ca

167d

ba00d

b90a

Up

sid

eD

own

Cat

her

ine

Wh

eel

Sh

e’s

My

Fri

end

(Ep

)A

lter

nati

ve0c5

cd89

d1f0

1f4

050a6984f7

30c3

753f

Urs

aM

ajo

rS

pac

eSt.

..C

ath

erin

eW

hee

lI

Want

To

Tou

chY

ou

Alt

ern

ati

ve57c0

bf9

63563ad

dec

e43c3

fe0f8

634f2

Wis

hY

ouW

ere

Her

eC

ath

erin

eW

hee

lL

ike

Cats

An

dD

ogs

Alt

ern

ati

ve15e7

dc5

b93fa

ceef

a498fa

26ea

53f4

d3

AF

oggy

Day

Bil

lie

Hol

iday

Bil

lie’

sB

est

Jazz

3e2

ca0d

5a5e2

b276412b

0c3

840301318

Bal

lad

Of

Hol

lis

Bro

wn

Bob

Dyla

nT

he

Tim

esT

hey

Ar.

..F

olk

125d

437

978169d

38a91d

b3948266ed

76

Cal

low

ayB

oog

ieB

igB

adV

ood

oo

Dad

dy

How

Big

Can

You

Get

Sw

ing

b4a60e9

f2d

faf1

c9980f0

2c3

ac2

8ea

64

Bla

ckC

row

Blu

esB

obD

yla

nA

noth

erS

ide

of

B..

.F

olk

d6d

27d

3441111c1

7903aff

45b

46ee

fdd

Tra

cyJac

ks

Blu

rP

ark

life

Alt

ern

ati

ve02f0

bff

9e4

ea3f9

ff0e5

1b

ebf7

5c1

347

Do

Over

Bra

idC

lose

rT

oC

lose

dIn

die

dd

f40b

64d

1e1

b80b

33033a979f4

5ab

81

IW

ann

aB

eL

ike

You

Big

Bad

Vood

oo

Dad

dy

Th

isB

eau

tifu

lL

ife

Sw

ing

9704fc

e21d

c958b

d8ca

24e8

3a3c3

4f0

3

Isis

Bob

Dyla

nD

esir

eF

olk

6f5

2ad

d5316a5600b

166a7f5

0ab

0b

77f

Lou

isia

na

Bar

ney

Kes

sel

To

Sw

ing

or

Not

t...

Jazz

b0c8

5e2

3f7

313fb

8c7

52d

5d

3f2

863115

Mr.

Pin

stri

pe

Su

itB

igB

adV

ood

oo

Dad

dy

Big

Bad

Vood

oo

Dad

dy

Sw

ing

ba6b

b2d

d5805918836b

0e9

e27cc

df7

34

Nas

hvil

leS

kyli

ne

Rag

Bob

Dyla

nN

ash

vil

leS

kyli

ne

Folk

576b

d7d

0e1

ad

c88d

8613fc

4898279965

Ple

dgi

ng

My

Tim

eB

obD

yla

nB

lon

de

on

Blo

nd

eF

olk

1fa

d92f

9e3

d4760a4d

0ab

a138b

b47c2

1

Sat

inD

oll

Bar

ney

Kes

sel

Th

eP

oll

Win

ner

sJazz

b579ef

af8

6ff

89c9

f7624f8

bb

48d

a885

Sh

eb

elon

gsto

me

Bob

Dyla

nB

rin

gin

gIt

All

B..

.F

olk

22b

7242

7876fb

a473570e1

079e2

a5377

89

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Sim

ple

Tw

ist

Of

Fat

eB

obD

yla

nB

lood

On

Th

eT

rack

sF

olk

d3266ca

04492026531d

dcc

9fd

5a5e2

16

Th

ese

Fool

ish

Th

ings

Ben

ny

Good

man

Ben

ny

Good

man

Jazz

a2b

fd0898943e0

023d

32492ae6

be7

4eb

Tim

eA

fter

Tim

eB

rian

Nov

aS

had

owO

fY

ou

rS

mil

eJazz

5c6

72114ef

76e9

2aa990a7867fd

0a76e

Tom

bst

one

Blu

esB

obD

yla

nH

ighw

ay61

Rev

isit

edF

olk

9ef

29e8

d38d

4d

54ee

e3cf

a11ea

d12850

Tw

enty

Yea

rsB

ill

Fri

sell

wit

hD

...

Bil

lF

rise

llw

ith

...

Jazz

18cc

8b

ea220b

8260e6

f7b

d27460eb

537

Ver

ona

Bil

lF

rise

llG

on

e,Ju

stL

ike

a..

.Jazz

4b

c872c

50cd

e31d

d2f9

0b

25f4

a3e9

bde

Vol

are

Bar

ney

Kes

sel

Th

eP

oll

Win

ner

s..

.Jazz

91c4

524c3

5d

ca0893b

111fb

0ab

245764

Wh

atA

reY

ouD

oin

g..

.B

arn

eyK

esse

lS

olo

Jazz

c6b

3836

562cd

e9619cd

935f6

5e6

ab

794

You

Kn

owY

ouW

ron

gB

igB

adV

ood

oo

Dad

dy

Sav

eM

yS

ou

lS

win

gf6

ab

963

a2e2

ad

6c7

bb

c617b

f53d

b801c

Sic

Tra

nsi

tG

lori

a...

.B

ran

dN

ewD

eja

Ente

ndu

Pu

nk

c13b

0d

274d

cd77d

6eb

c0421076c3

4a35

(Inte

rmis

sion

)B

ane

ItA

llC

om

esD

own

...

Pu

nk

55ca

42b

4b

f4125707e0

307b

d6e1

ec3d

f

AB

reak

,A

Pau

seA

sT

all

As

Lio

ns

As

Tall

As

Lio

ns

Ind

ie4b

ae0

3972d

8173b

5b

a2625a5c1

865fc

2

Ain

tL

ove

Gra

nd

Atr

eyu

Su

icid

eN

ote

san

d..

.M

etal

340f1

9b

946306d

17b

075fb

64cc

fcb

746

An

dth

eW

ick

Bu

rnt

...

As

Tal

lA

sL

ion

sB

lood

An

dA

ph

ori

sms

Ind

ie8ad

8b

b13d

54d

39856e1

128839e1

b799b

Tea

rsF

orT

he

Sh

eep

Atm

osp

her

eL

ucy

Ford

,T

he

At.

..H

ip-H

op

7463405b

5842a4b

eefe

e0d

8d

fa92b

435

Th

eS

kin

ny

Atm

osp

her

eW

hen

Lif

eG

ives

Y..

.H

ip-H

op

990988ac6

f20306e9

9f5

389678237000

Au

gust

Ava

ilO

ver

Th

eJam

esP

un

kd

32c3

bcf

8a1276f2

9b

6f6

42fe

6e8

8640

Lar

edo

Ban

dO

fH

orse

sIn

fin

ite

Arm

sIn

die

99644d

04520b

20871d

370b

6039a5e6

36

Bew

ild

erB

adly

Dra

wn

Boy

Th

eH

ou

rO

fB

ewil

...

Ind

ie6732556976b

41cc

5b

bb

ef5b

251966408

Bir

dS

ings

Why

Th

e..

.A

tmos

ph

ere

Sev

en’s

Tra

vel

sH

ip-H

op

cad

ffc5

f20ac0

73187d

39c1

69ec

c5c0

d

90

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Bot

hG

un

sB

lazi

ng

Ban

eH

old

ing

Th

isM

om

ent

Pu

nk

10273ad

1f0

6602358d

7d

a06f3

8d

5e2

af

Can

’tS

top

ItB

adR

elig

ion

Th

eP

roce

ssO

fB

e...

Pu

nk

0491823d

d23c8

3042447f8

eb0c5

5d

258

Car

ryM

eH

ome

Atm

osp

her

eS

ad

Clo

wn

Bad

Sp

r...

Hip

-Hop

3b

6f5

6b929fb

e9b

e5ee

37deb

f34d

154a

Cri

cifi

edA

uto

mat

ic7

Au

tom

ati

c7

Pu

nk

51b

6ef

ecd

60f2

80887d

08a49c0

3c9

dc2

Cu

rren

tS

tatu

sA

tmos

ph

ere

Ove

rcast

!H

ip-H

op

d3aee

9aa01d

e99949c1

91cc

97cb

30281

Em

pir

eG

rad

eA

ud

iocr

ush

So

You

Call

Th

ese.

..In

die

7e9

f4a08a85b

678c4

1574280d

0b

68399

Giv

eM

eA

tmos

ph

ere

God

Lov

esU

gly

Hip

-Hop

e299516fb

2c5

3f1

9e3

def

88b

d848921e

How

Mu

chIs

En

ough

Bad

Rel

igio

nS

uff

erP

un

k3ad

ec87

758303140197ed

bb

801c8

cd87

N30

Ava

ilO

ne

Wre

nch

Pu

nk

eef9

5f8

cc3a72147b

49204c4

b0ce

54ad

No

Dir

ecti

onB

adR

elig

ion

Gen

erato

rP

un

k809704371972ee

f1c6

9e1

fbcf

d0ad

7b

c

No

On

e’s

Gon

na

Lov

e...

Ban

dof

Hor

ses

Cea

seto

Beg

inIn

die

69284508d

dd

1a1907ca

27ff

f1cf

b11b

a

On

eA

rmed

Sci

ssor

At

Th

eD

rive

-In

Rel

ati

on

ship

Of

C..

.P

un

kf3

216f4

bc2

0fc

23f4

c18715739696c1

7

Ou

rS

wor

ds

Ban

dO

fH

orse

sE

very

thin

gA

llT

h..

.In

die

9c0

9108527cc

ceec

090b

10ef

e85321b

1

Sin

gle

Bad

Ast

ron

au

tH

ou

ston

:W

eH

ave

...

Pu

nk

c568800d

5b

68f5

89c8

0874e7

96d

14c0

8

Sn

akes

Am

ong

Us

Ban

eG

ive

Blo

od

Pu

nk

fe3883d

f6ee

fc26a8a9683487c0

cac6

d

Sor

eL

oser

sB

igw

igA

nIn

vit

ati

on

To

...

Pu

nk

d0d

74ab

7101c0

ac1

c70145b

06e7

bcb

a5

Sou

nd

Of

AG

un

Au

dio

slav

eR

evel

ati

on

sA

lter

nati

vea0d

63d

b1321c5

30fc

620ea

3c2

023c5

d9

Str

ange

rT

han

Fic

tion

Bad

Rel

igio

nS

tran

ger

Th

an

Fic

...

Pu

nk

35b

6938

5d

b8216401a5a1b

0658ee

aa86

Th

eyA

llG

etM

adA

t...

Atm

osp

her

eS

ad

Clo

wn

Bad

Win

...

Hip

-Hop

e6d

608e

7d

90f4

2580d

edf7

69d

ae5

83f6

Tor

ture

sO

fT

he

Dam

ned

Bay

sid

eB

aysi

de

Pu

nk

e77b

b5c5

238d

9487ff

8f4

535329a8831

91

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Tu

esd

ayA

vail

4A

MF

rid

ayP

un

k9cc

afc

04fd

05923d

ff55f6

a9200cc

df1

Tu

nin

gA

vail

Dix

ieP

un

k40a8d

ed9d

f15e3

38ca

cd70ef

57e9

87d

7

Urs

aM

inor

At

Th

eD

rive

-In

VA

YA

Pu

nk

c868fa

a105e3

ea9ee

68f3

f9d

592e8

c0e

RF

TC

Atm

osp

her

eS

ad

Clo

wn

Bad

Su

m..

.H

ip-H

op

ef85a6b

c3ce

70aad

c15ad

197b

2c8

32aa

Wat

chO

ut

Atm

osp

her

eY

ou

Can

’tIm

agin

e...

Hip

-Hop

40383eb

3d

691c4

55d

385b

322a8220775

Bec

ame

Atm

osp

her

eT

he

Fam

ily

Sig

nH

ip-H

op

092d

afa

fee7

fb65843c2

1b

a2b

503e1

4b

Ju

stIn

Tim

eB

arn

eyK

esse

lL

et’s

Cook!

Jazz

0983f0

f4f8

033ca

8b

4d

27391b

d2955a1

My

Rev

erie

Bar

ney

Kes

sel

Mu

sic

To

Lis

ten

To

Jazz

a070a0f7

661ea

0d

0702a37746f3

a4289

On

aS

low

Boa

tto

C..

.B

arn

eyK

esse

lK

esse

lP

lays

Sta

n..

.Jazz

17b

1a07

6f1

ce9a42123fd

265f3

b39cc

1

Poor

Bu

tter

fly

Bar

ney

Kes

sel

Poor

Bu

tter

fly

Jazz

bc7

6e2

768b

a5787614aaf5

2c8

f9c3

083

Th

eL

ittl

eR

hu

mb

aB

arn

eyK

esse

lP

oll

Win

ner

sT

hre

eJazz

1a332e9

4c7

6633496a902e0

28203e8

52

You

Can

’tT

ake

ItW

...

As

Tal

lA

sL

ion

sY

ou

Can

’tT

ake

It..

.In

die

381c8

d428e6

03451499b

e4b

75368fc

a1

Sca

lpA

tmos

ph

ere

To

All

My

Fri

end

s...

Hip

-Hop

d26b

8d

ad

a3489fe

6611536cb

42934444

Anti

soci

alA

nth

rax

Sta

teO

fE

up

hori

aM

etal

446634261b

64a37f1

1d

b266637e0

d798

Ble

edT

he

Fre

akA

lice

InC

hai

ns

Face

lift

Alt

ern

ati

ve39185a4d

0ca

09b

460ce

db

b4a5930d

73f

Con

tin

enta

lA

lkal

ine

Tri

oG

ood

Mou

rnin

gP

un

k8c9

19d

22f6

5886e9

86ed

d1b

cb08c4

43a

Dea

dO

nT

he

Flo

orA

lkal

ine

Tri

oT

his

Ad

dic

tion

Pu

nk

0b

bd

692514093e4

271a2e3

57b

37210e0

Dow

nIn

AH

ole

Ali

ceIn

Ch

ain

sD

irt

Alt

ern

ati

ve634a0e5

eb7094f8

52ed

0b

c46916ca

b0a

Efi

lnik

ufe

sin

(N.F

.L.)

Anth

rax

Am

on

gT

he

Liv

ing

Met

al

ad

d816c0

c4aa06075d

69aab

efce

ec6c2

F**

*Y

ouA

uro

raA

lkal

ine

Tri

oM

ayb

eI’

llC

atc

h..

.P

un

k975a6b

89f5

62d

7ff

0e0

bae7

8c8

8ce

b98

92

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Fu

nn

els

All

anH

old

swort

hA

llN

ight

Wro

ng

Jazz

c5fd

f8b

afa

c24522d

6992ab

13796acb

6

Hea

ven

Bes

ide

You

Ali

ceIn

Ch

ain

sA

lice

InC

hain

sA

lter

nati

ve3092cd

fb02e0

eafc

081afe

538fe

bcc

dc

IL

ied

My

Fac

eO

ffA

lkal

ine

Tri

oA

lkali

ne

Tri

oP

un

ke7

8883f7

cb4ee

257419e8

6eb

cafe

ba6d

Mer

cyM

eA

lkal

ine

Tri

oC

rim

son

Pu

nk

bd

06f6

ba5fb

ac6

e8fa

66c8

6f8

53c7

483

No

Excu

ses

Ali

ceIn

Ch

ain

sJar

Of

Fli

esA

lter

nati

ve596ec

c48411f8

20a65b

9d

bb

3ed

7ce

a1b

Nos

eO

ver

Tai

lA

lkal

ine

Tri

oG

od

dam

nit

Pu

nk

223f3

5478d

c7674d

c8b

401af5

e847c8

4

Slu

dge

Fac

tory

Ali

ceIn

Ch

ain

sM

TV

Un

plu

gged

Alt

ern

ati

ve8ec

0a73f3

122c4

a52ea

f82cc

e7a0256b

Sta

min

aA

llE

lse

Fai

lsT

he

Ch

ase

Of

Gain

Pu

nk

01cb

3a2

1ac4

a211b

df5

839d

2e4

80a225

Stu

pid

Kid

Alk

alin

eT

rio

Fro

mH

ere

toIn

fi..

.P

un

k20d

356a

4a20f9

d7713acc

8d

c61fd

8536

Th

eC

arou

sel

As

Tal

lA

sL

ion

sL

afc

ad

ioIn

die

2e8

a24929fc

d2647c0

4d

976e1

7e7

c15d

Tok

yoD

ream

All

anH

old

swort

hR

oad

Gam

esJazz

f78b

a49

afb

41d

3ef

5d

6039e1

5f6

a3fe

1

Tu

nin

gA

vail

Liv

eat

the

Bott

o..

.P

un

k48f0

29b

7ef

de3

4fa

94fa

25b

4943a798a

Cap

acit

yT

oC

han

geC

ath

erin

eW

hee

lJu

dy

Sta

rin

gA

tt.

..A

lter

nati

ve667d

70b

9413b

eef7

87667898d

c87ec

0a

Eve

ryT

hu

gN

eed

sA

...

Alk

alin

eT

rio

Dam

nes

iaP

un

k680ed

e279047d

4a5e4

07f9

f3f0

f89cb

2

Ove

rA

nd

Ou

tA

lkal

ine

Tri

oA

gony

An

dIr

ony

Pu

nk

31af0

53af9

09a5ed

b5d

2f8

f526a25b

7e

Sto

p!

Aga

inst

Me!

New

Wav

eP

un

k17c3

ebc0

e889d

205ca

22aa94d

35e5

e03

Bot

hF

eet

InA

lex

Skol

nic

kT

rio

Tra

nsf

orm

ati

on

Jazz

5c9

ad

b4d

a2487d

302fb

a11b

3d

a4e5

531

Ch

emic

alP

arty

Gav

inD

eGra

wC

hari

ot

Pop

Rock

577d

a2e

958ac0

f2f8

4990a0b

18b

c2a3a

Des

troy

Wh

atD

estr

o...

Aga

inst

All

Au

thori

tyD

estr

oyW

hat

Des

t...

Pu

nk

7d

9f9

549c6

e6775643fd

329987420e3

5

Let

ter

from

Ind

iaA

lD

iM

eola

,P

aco

D..

.T

he

Gu

itar

Tri

oJazz

12b

3ab

ece0

f2c6

f7e8

a90ea

349140596

93

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Mov

ing

Mol

ecule

sA

gain

stT

omorr

ow’s

Sky

Th

eL

ost

Tap

esIn

die

14337afe

fd7d

fbab

996f6

d4b

ab

3608d

3

Per

fect

ion

All

Mass

Ner

der

Pu

nk

5cc

39c4

f4afc

e0fd

ed852457a2094213

Slu

rrin

gth

eR

hyth

ms

Aga

inst

Me!

As

Th

eE

tern

al

Co..

.P

un

kd

769a05

bc7

a39725429ce

164940f6

1ad

Tak

ing

Not

esA

gain

stT

omorr

ow’s

Sky

Ju

mp

Th

eH

edges

F..

.In

die

07610b

2e5

46c0

fc622af4

333b

772104c

Fro

mH

erL

ips

To

Go.

..A

gain

stM

e!S

earc

hin

gfo

ra

F..

.P

un

kb

933759

5041c0

84c2

feb

a7a8b

7d

04964

Scr

eam

ItU

nti

lY

ou..

.A

gain

stM

e!R

einve

nti

ng

Axl

Rose

Pu

nk

67c4

658ea

ec4fd

721a5569c8

f8fd

7a92

Gir

l’s

Not

Gre

yA

FI

Sin

gT

he

Sorr

owP

un

k7d

bc6

f9f5

1f4

b660795d

c06b

07fd

71f7

Sta

nd

InL

ine

Aga

inst

All

Au

thori

tyA

llF

all

Dow

nP

un

k1d

ef858

db

1301b

15e9

377a8a5d

8e9

a2b

Th

eC

hec

kere

dD

emon

AF

IA

nsw

erT

hat

An

dS

...

Pu

nk

82eb

7ae

9f1

7b

cbf1

4f3

c2366a32e0

4c2

Th

eR

ipA

Wil

hel

mS

crea

mM

ute

Pri

nt

Pu

nk

9864e4

1a64ee

a592a025a12cc

5d

b9f2

1

Me

Vs.

Mor

riss

eyIn

...

AW

ilh

elm

Scr

eam

Ru

iner

Pu

nk

b3c0

59a

dc3

fc5163a0e8

21c3

bafd

bc2

a

My

Lit

tle

Wor

ld88

Fin

gers

Lou

ieB

ehin

dB

ars

Pu

nk

0ec

2c6

fbd

488058125a064564927f2

80

Th

eP

raye

rP

osit

ion

AF

IB

lack

Sail

sIn

Th

...

Pu

nk

a08464d

82d

1c0

4b

b86a430668b

59e1

21

Th

eory

Of

Rev

olu

tion

AF

IV

ery

Pro

ud

Of

Ya

Pu

nk

3aa7d

d13069847c3

b312fa

ea3606b

c84

Sle

epin

gB

eau

tyA

Per

fect

Cir

cle

Mer

de

Nom

sM

etal

a1ac9

410542acf

e936cc

e30a8797c7

0e

Get

Mad

,Y

ouS

onof

...

AW

ilh

elm

Scr

eam

Care

erS

uic

ide

Pu

nk

9d

39915

37c1

3d

7456c0

88727d

ad

9b

e88

Th

eC

rew

7S

econ

ds

Th

eC

rew

Pu

nk

379e2

be9

c252608a31b

083004b

98600a

Rel

ativ

eW

ays

...A

nd

You

Wil

lK

no..

.S

ou

rce

Tags

&C

od

esIn

die

be0

3b

3d

8f3

60fa

3fa

e53a1137394658e

You

rsp

irit

’sal

ive

Dro

pkic

kM

urp

hys

Th

eW

arr

ior’

sC

od

eP

un

kb

dc7

f9fe

909c7

419454e2

f1cf

6e9

d195

Ice

Mac

hin

eD

epec

he

Mod

eS

pea

k&

Sp

ell

New

Wav

e91b

b30ca

028456ca

fa4e5

6aeb

c9494c4

94

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

IfY

ouW

ant

(Liv

ei.

..D

epec

he

Mod

eS

om

eG

reat

Rew

ard

New

Wav

e3a3625f6

14956af7

04f4

a4c1

f1a8d

7a5

My

Sec

ret

Gar

den

(L..

.D

epec

he

Mod

eA

Bro

ken

Fra

me

New

Wav

e0d

27382

498312ae8

5aad

b5778407e8

8b

Som

eth

ing

toD

oD

epec

he

Mod

eS

om

eG

reat

Rew

ard

New

Wav

ef8

4f7

cb12e0

768ee

a752b

2f1

250544c5

Th

eG

reat

Ou

tdoor

s!D

epec

he

Mod

eC

on

stru

ctio

nT

ime.

..N

ewW

ave

1595849c9

364ef

b974765af4

ed25d

311

Wal

kA

way

Dro

pkic

kM

urp

hys

Bla

ckout

Pu

nk

a5ac7

bfd

d4cc

c7a0b

8f5

60a7e1

5aa963

Sco

ttis

hW

ind

sF

righ

ten

edR

ab

bit

AF

righte

ned

Rab

b..

.In

die

b98c3

6369867ab

e3a05627ef

55224f9

1

We’

reB

reak

ing

Up

Aga

inst

Me!

Wh

ite

Cro

sses

Pu

nk

3934d

ef3e5

fb05d

35e4

ae5

81358d

cc74

Lif

elin

e(L

ive)

Cat

her

ine

Wh

eel

Wis

hvil

le(B

onu

s..

.A

lter

nati

vec2

8214a2cb

48a33b

af8

ca1c1

419816f6

Del

icio

us

(Liv

e)C

ath

erin

eW

hee

lM

aS

oli

tud

a[D

isc

2]

Alt

ern

ati

vead

65cc

dd

3aff

c7a5b

8a6d

27a96e5

633e

20th

Cen

tury

Tow

ers

Dea

thC

abfo

rC

uti

eT

he

Sta

bil

ity

EP

Ind

ie886f0

d39e7

5b

3334200c2

e3ed

867ff

3f

Mak

esth

eS

un

Com

eO

ut

Atm

osp

her

eS

ad

Clo

wn

Bad

Fal.

..H

ip-H

op

d972cd

8fc

95a917d

13ab

0f8

5086c1

e47

Fu

llM

oon

Atm

osp

her

eS

tric

tly

Lea

kage

Hip

-Hop

96ca

9225f6

99c2

0b

fbce

53cb

a562fc

ce

Th

eR

opes

Atm

osp

her

eL

eak

At

Wil

lH

ip-H

op

b1b

9b

f2c3

b10d

228d

4ee

9b

7d

536496c3

Ju

de

Law

An

dA

Sem

e...

Bra

nd

New

You

rF

avori

teW

ea..

.P

un

k85b

b505acf

410c4

7994d

43c4

33b

f0fa

b

By

My

Sid

eC

opel

and

Eat,

Sle

ep,

Rep

eat

Ind

ieff

b0157a

b1af8

4b

d09d

346f1

422ef

3d

a

Mar

chin

gB

and

sof

M..

.D

eath

Cab

for

Cu

tie

Pla

ns

Ind

ie9d

05518

bd

5418826eb

3e2

7d

e3f5

b1b

62

Wor

ldIn

My

Eyes

Dep

ech

eM

od

eV

iola

tor

New

Wav

e2cc

56cf

ceb

0d

a75477ee

e1eb

f7639d

9a

ID

on’t

Th

ink

Din

osau

rJr

Han

dIt

Ove

rA

lter

nati

ve800e5

3e1

02c5

dd

1c5

c5421b

55047ea

f9

Eve

nY

ouD

inos

aur

Jr

Wit

hou

ta

Soun

dA

lter

nati

ve9f2

d333

976d

ec167d

6fd

d7ff

bae4

3c6

8

Ou

tT

her

eD

inos

aur

Jr.

Wh

ere

You

Bee

nA

lter

nati

vecd

64872

3346a4e0

8330b

26b

f1744ea

81

95

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Day

One

Exp

losi

ons

InT

he

Sky

Tra

vels

InC

on

sta..

.In

die

db

e72d

22ac5

3d

00d

587e7

6d

d4b

0487c6

Hid

den

Tra

ckB

ran

dN

ewT

he

Dev

ilA

nd

God

...

Pu

nk

eb24454

91602c0

45a5a9688f9

45a5929

Bee

tleb

um

Blu

rB

lur

Alt

ern

ati

vefa

c5ca

2618f4

c77609c9

27c0

7e6

f434f

Lov

eS

ick

Bob

Dyla

nT

ime

Ou

tO

fM

ind

Folk

55e0

8b

ad

e8946059d

8c2

b9ef

0a494060

Bor

edT

oT

ears

Bla

ckL

abel

Soci

ety

Son

icB

rew

Met

al

9c9

a648843b

f6b

0ea

ab

4313548b

a72c2

An

gel

Fal

lsB

raid

Fra

nkie

Wel

fare

B..

.In

die

5eb

d9e3

36b

72d

b92f9

2813160a91d

89e

Ele

ph

ant

Bra

idM

ovie

Mu

sic

vol.

Tw

oIn

die

9f2

36988233f0

44f6

01277b

f342d

4b

45

Bu

rned

Bet

ter

Th

anE

zra

Bef

ore

Th

eR

ob

ots

Alt

ern

ati

ve1c2

c018a01b

878736400b

889d

337b

a9a

Car

olin

aB

lues

Blu

esT

ravel

erS

traig

ht

on

Til

l..

.A

lter

nati

vea42ff

aea

b9a4e8

a1c7

ff12a70ca

ac5

a1

Cra

zyO

rH

igh

Bla

ckL

abel

Soci

ety

Han

gov

erM

usi

cV

o..

.M

etal

904b

00f

be6

f2a1eb

c9c8

793b

105eb

f78

Dis

farm

erT

hem

eB

ill

Fri

sell

Dis

farm

er(S

tan

dard

)Jazz

d0851e1

98cf

58a6e4

2f2

85fe

b55eb

cf4

Dol

lF

oo

Fig

hte

rsC

olo

ur

&T

he

Sh

ap

eA

lter

nati

veb

eafa

a7cc

e81925c0

4583a09c2

a0e0

57

dru

gsan

dal

coh

ol(.

..B

rian

Set

zer

13

Rock

abil

ly254ee

d1fa

2e0

7e4

2d

c840d

4ad

5d

0aed

b

Eve

ryth

ing

Zen

Bu

shS

ixte

enS

ton

eA

lter

nati

ve1d

b566692ab

782878a6f8

b436b

a930fa

Fir

eIt

Up

Bla

ckL

abel

Soci

ety

Mafi

aM

etal

3032f9

5b

41b

f245a3b

62b

fc66f8

28161

God

Insi

de

My

Hea

dC

ath

erin

eW

hee

lH

ap

py

Day

sA

lter

nati

vec7

8a496a20276e6

cd01862fa

3b

4ff

bd

5

InT

he

Blo

od

Bet

ter

Th

anE

zra

Del

uxe

Alt

ern

ati

ve2e4

9c9

81220459333b

6fa

ac4

b4ac3

2e7

Intr

oC

ath

erin

eW

hee

lA

dam

An

dE

ve-

Me.

..A

lter

nati

ve90105b

95c4

1e5

67d

3ce

f29732b

13f4

80

It’s

AL

ong

Sto

ryB

ill

Fri

sell

and

85..

.S

ign

Of

Lif

eJazz

6b

497b

483d

b4e8

cbaf2

3fd

52e3

db

96cf

Jin

gle

Bel

lsB

ing

Cro

sby

Fra

nk

...

Ch

rist

mas

Jazz

84d

1122

713ac2

48b

dd

440aef

a8b

027ac

96

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Kil

lR

hyth

mC

ath

erin

eW

hee

lC

hro

me

Alt

ern

ati

ve01eb

135

a36b

5b

da25ea

6443d

d8c4

847a

Kin

gO

fN

ewO

rlea

ns

Bet

ter

Th

anE

zra

Fri

ctio

n,

Baby

Alt

ern

ati

ve2d

85132

134f1

0d

c4a2ef

d7d

bd

88ea

223

Nar

cole

psy

Ben

Fol

ds

Fiv

eT

he

Un

au

thori

zed

...

Ind

iefa

b8789

e67f0

23e8

fd71a696c2

049d

56

Old

Lad

yB

igw

igU

n-M

erry

Mel

od

ies

Pu

nk

c6cf

7c7

0d

c137c7

094f6

e73c9

2101fd

d

On

eA

ngr

yD

war

fA

nd

...

Ben

Fol

ds

Fiv

eW

hate

ver

An

dE

ver.

..In

die

2b

1d

a5185c7

c7fe

70d

4c4

be1

6e6

2e0

f6

Rhym

in&

Ste

alin

Bea

stie

Boy

sL

icen

sed

To

Ill

Hip

-Hop

021216c9

6669c0

cb818865f9

a6108962

Ru

n-A

rou

nd

Blu

esT

ravel

erF

ou

rA

lter

nati

ved

267fd

3e6

a876a756b

ae3

522ef

2954f4

Sav

age

Cac

oph

ony

Sp

eed

Met

al

Sym

ph

ony

Met

al

91ca

a9608f2

1444378cd

13322ab

d8692

Sou

nd

sL

ike

Vio

len

ceB

raid

Mov

ieM

usi

cV

ol.

On

eIn

die

22a9095ed

b9986657f0

029d

4ad

c9b

c9b

Ste

reot

yp

esB

lur

Th

eG

reat

Esc

ap

eA

lter

nati

ve3f0

e1029f6

93d

9601754afe

19d

978685

Sti

llB

igw

igS

tay

Asl

eep

Pu

nk

888484c9

cb973e5

87eb

e69206f6

0a1d

8

Su

reS

hot

Bea

stie

Boy

sIl

lC

om

mu

nic

ati

on

Hip

-Hop

bc2

760f

6aa1a6a5e1

88453a011758524

Tel

lM

eB

osto

nG

reate

stH

its

Rock

26e1

6008421974c2

9d

01ca

263b

86f1

de

Tex

ture

Cat

her

ine

Wh

eel

Fer

men

tA

lter

nati

ve6e0

a8e5

8514c8

9ed

ae9

df4

15e7

c2c7

9e

Tri

na

Mag

na

Blu

esT

ravel

erS

ave

His

Sou

lA

lter

nati

ve55b

d0f0

40b

5ae4

f079e6

8e8

096e5

6462

Vic

esB

ran

dN

ewD

ais

yP

un

k2b

db

917e8

9f4

bc1

5d

4b

37e6

d7b

3c8

72a

Au

tob

iogr

aphy

Bra

idL

uck

yT

oB

eA

live

Ind

ie0a87b

c5863a3964c5

6855c2

17cd

674d

e

Blo

win

’In

Th

eW

ind

Bob

Dyla

nT

he

Fre

ewh

eeli

n’

...

Folk

8b

fa9fe

28150e8

1883b

1a7675d

5a60f1

Bro

ken

Nos

e(L

ive

Xfm

)C

ath

erin

eW

hee

lB

roke

nN

ose

[Dis

c2]

Alt

ern

ati

ve9c9

d534

7a003e4

3e7

9c9

2b

e48171195a

Bro

ken

Nos

e(S

ingl

e...

Cat

her

ine

Wh

eel

Bro

ken

Nose

[Dis

c1]

Alt

ern

ati

vee8

f6160b

f992cc

3ea

2456a2c7

5d

cfd

69

97

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Cra

nk

Cat

her

ine

Wh

eel

Cra

nk

Alt

ern

ati

ve6d

97a63

e6a61cc

9b

8ee

0b

cc0e9

871b

0b

Hea

l2

Cat

her

ine

Wh

eel

Lik

eC

ats

An

dD

ogs

Alt

ern

ati

vea5101389f4

2d

0b

3e5

48d

b53d

6e5

bd

840

IW

ant

To

Tou

chY

ouC

ath

erin

eW

hee

lI

Want

To

Tou

chY

ou

Alt

ern

ati

vec9

45ec

def

a8d

d72248c9

0792754d

cab

1

Ju

dy

Sta

rin

gA

tT

he.

..C

ath

erin

eW

hee

lJu

dy

Sta

rin

gA

tT

...

Alt

ern

ati

ve51e0

e2af2

ae3

99d

043d

2f5

bc5

67ab

157

Ma

Sol

itu

da

Cat

her

ine

Wh

eel

Ma

Soli

tud

a[D

isc

1]

Alt

ern

ati

vee8

69f9

b2ed

164758777ea

a823c3

826c1

My

Bab

yS

mok

esB

raid

Th

eA

ge

of

Oct

een

Ind

ie981113f6

bce

1a0ee

5fe

706569f9

0e5

2e

Th

eB

oyW

ho

Blo

cked

...

Bra

nd

New

Th

eH

oli

day

EP

Pu

nk

003f7

b27ab

972166f3

164747d

9fa

a9d

b

Th

eN

ewN

ath

anD

etr.

..B

raid

Fra

me

&C

anva

sIn

die

560a7d

fac4

4589265261982d

4d

3e4

ea7

All

IR

eall

yW

ant

t...

Bob

Dyla

nA

noth

erS

ide

of

B..

.F

olk

a5d

c53a

7d

f87ed

122b

1a49e8

2b

37ce

e4

Be

Dee

dle

De

Do

Bar

ney

Kes

sel

Th

eP

oll

Win

ner

s..

.Jazz

b601c4

f90d

94262b

978b

87ae3

c4f0

c58

Beg

inth

eB

lues

Bar

ney

Kes

sel

To

Sw

ing

or

Not

t...

Jazz

f098ea

f10ca

0d

6142d

1cc

9053cb

29d

0f

Big

An

dB

adB

igB

adV

ood

oo

Dad

dy

Th

isB

eau

tifu

lL

ife

Sw

ing

2d

1ce

aa809d

3969c4

938550342e5

8e6

8

Com

eO

nW

ith

Th

e”c

...

Big

Bad

Vood

oo

Dad

dy

How

Big

Can

You

Get

Sw

ing

4194ef

67d

ab

865407f6

ad

92cf

c8a6784

Blu

esfo

rL

osA

nge

les

Bil

lF

rise

llG

on

e,Ju

stL

ike

a..

.Jazz

e7d

8065

f07b

c6a4ef

53e2

c7465ee

293d

Gir

ls&

Boy

sB

lur

Park

life

Alt

ern

ati

ve3e4

b2cc

ead

1cf

6ce

2c1

6afe

aa78c8

3cd

Th

eR

ight

Tim

eB

raid

Clo

ser

To

Clo

sed

Ind

ie8e2

8ca

263790b

965428cd

59362b

f00eb

Bra

zil

Bar

ney

Kes

sel

Solo

Jazz

d36d

e4eb

6e9

0b

7c8

245f8

5fc

812e0

238

Gir

lF

rom

Th

eN

orth

...

Bob

Dyla

nN

ash

vil

leS

kyli

ne

Folk

88a53710e1

e8d

b6e9

929817fb

1c5

e0b

7

Hu

rric

ane

Bob

Dyla

nD

esir

eF

olk

bb

ba56

226b

d4c7

083126f5

be0

640aaff

Jor

du

Bar

ney

Kes

sel

Th

eP

oll

Win

ner

sJazz

550e8

ea3c3

ce4fa

23e2

930b

0c5

ecb

4cb

98

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Let

’sD

ance

Ben

ny

Good

man

Ben

ny

Good

man

Jazz

cb48b

927b

58661b

28192d

2f3

fbf0

0f0

c

Lik

eA

Rol

lin

gS

ton

eB

obD

yla

nH

ighw

ay61

Rev

isit

edF

olk

9b

38e1

1212b

5d

f8095691b

22c5

2b

c9a4

Ou

tlaw

sB

ill

Fri

sell

wit

hD

...

Bil

lF

rise

llw

ith

...

Jazz

c07ef

027168b

d5cf

c42275d

e3c8

83851

Rai

ny

Day

Wom

en12

...

Bob

Dyla

nB

lon

de

on

Blo

nd

eF

olk

bea

2ad

16e2

35904ab

d835afa

e6b

bcd

f1

Su

bte

rran

ean

hom

esi.

..B

obD

yla

nB

rin

gin

gIt

All

B..

.F

olk

d9d

9f7

1c9

3b

23fb

242b

169279ce

5b

557

Tan

gled

Up

InB

lue

Bob

Dyla

nB

lood

On

Th

eT

rack

sF

olk

1e9

f981e0

33ca

604705863e4

eafc

41f9

Th

eB

oog

ieB

um

per

Big

Bad

Vood

oo

Dad

dy

Big

Bad

Vood

oo

Dad

dy

Sw

ing

f381b

78177016f7

b01b

49393422b

bf4

5

Th

eS

had

owO

fY

our

...

Bri

anN

ova

Sh

ad

owO

fY

ou

rS

mil

eJazz

9e0

c2f9

6b

d5d

69f4

fa7a581ac4

2f8

1b

9

Th

eT

imes

Th

eyA

re...

Bob

Dyla

nT

he

Tim

esT

hey

Ar.

..F

olk

19b

c1a4

aeb

2d

572b

5924cd

0ef

b8a6d

f3

Wh

atA

Lit

tle

Moon

l...

Bil

lie

Hol

iday

Bil

lie’

sB

est

Jazz

553a5e6

e98fb

86e8

49b

7ce

7c3

d61eb

cc

Zig

Zag

gity

Woop

Wo.

..B

igB

adV

ood

oo

Dad

dy

Sav

eM

yS

ou

lS

win

g52c3

ac3

98ad

ef617139b

99a338a73eb

9

Tau

tou

Bra

nd

New

Dej

aE

nte

ndu

Pu

nk

441ec

43b

52888c5

26c2

b037a5ce

9d

b88

1000

Mor

eF

ool

sB

adR

elig

ion

Su

ffer

Pu

nk

0b

c21a4

ee8d

83af1

32b

223b

e094d

2170

66th

Str

eet

Atm

osp

her

eS

ad

Clo

wn

Bad

Win

...

Hip

-Hop

84e3

05c0

72b

049690cd

548048f8

ca361

Lik

eT

od

ayA

tmos

ph

ere

Lu

cyF

ord

,T

he

At.

..H

ip-H

op

d436b

127d

433d

9d

37c6

a3124a03f9

345

Pu

pp

ets

Atm

osp

her

eW

hen

Lif

eG

ives

Y..

.H

ip-H

op

1373cd

e6b

aae9

c371f8

90c0

2a0551d

f0

Com

pli

men

tsB

and

Of

Hor

ses

Infi

nit

eA

rms

Ind

ie216d

d2eb

1636ff

3924554e7

73b

2ec

9f4

Bri

efD

escr

ipti

onA

tmos

ph

ere

Ove

rcast

!H

ip-H

op

c3d

9542

b89a73f6

45204d

d5061d

6d

3a0

Cle

arC

utt

ing

Bad

Ast

ron

au

tH

ou

ston

:W

eH

ave

...

Pu

nk

22b

b4ed

61d

e116d

31c4

f6544ed

965728

Clo

ne

Ava

ilD

ixie

Pu

nk

b9f2

8b

357b

5fd

cf1816655ff

2b

6c8

9ad

99

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Cou

nt

Me

Ou

tB

ane

Hold

ing

Th

isM

om

ent

Pu

nk

b3f8

9d

e5482e0

520a8d

f561f2

9182951

Dan

cin

gin

the

Rea

r...

As

Tal

lA

sL

ion

sB

lood

An

dA

ph

ori

sms

Ind

ieb

c294c9

e12743fc

03aa7aeb

81c3

5855e

Dev

otio

n&

Des

ire

Bay

sid

eB

aysi

de

Pu

nk

f7e1

3b

88acc

62b

c3c1

8227e2

bc1

e8501

Dil

ated

Atr

eyu

Su

icid

eN

ote

san

d..

.M

etal

840b

589

669d

ec3869eb

d79c7

b8d

65fd

b

Eve

ryb

od

y’s

Sta

lkin

gB

adly

Dra

wn

Boy

Th

eH

ou

rO

fB

ewil

...

Ind

ie78b

090e

3c9

5ec

a34022b

ed39b

8544459

Good

Dad

dy

Atm

osp

her

eS

ad

Clo

wn

Bad

Sp

r...

Hip

-Hop

432e3

df7

2d

a43853875a96cf

35e8

cef1

InH

ere

Au

dio

cru

shS

oY

ou

Call

Th

ese.

..In

die

b6fb

017d

9500c9

175b

a9ad

d98c8

3f4

76

Lea

veM

ine

To

Me

Bad

Rel

igio

nS

tran

ger

Th

an

Fic

...

Pu

nk

446ca

c33288717ab

43d

4e7

8a99925c5

1

Lu

cky

One

Au

tom

atic

7A

uto

mati

c7

Pu

nk

04f2

b2a

28d

5f9

466b

911c8

11402a4b

a5

New

2A

vail

Ove

rT

he

Jam

esP

un

ke9

4b

691

8cb

41d

5301d

ea151e7

4b

8155c

Od

eto

LR

CB

and

ofH

orse

sC

ease

toB

egin

Ind

ie819aa4e0

b6e1

7a2a89ed

e2f3

11fa

cb37

On

eA

nd

Th

eS

ame

Au

dio

slav

eR

evel

ati

on

sA

lter

nati

veec

e47c1

6b

22b

11d

2108728237a5a7cf

7

Ord

erA

vail

4A

MF

rid

ayP

un

kc3

31947c0

a20c5

360b

bd

2d

52e7

f68162

Pat

tern

Aga

inst

US

erA

tT

he

Dri

ve-I

nR

elati

on

ship

Of

C..

.P

un

kc1

fdaa9

8d

f4f2

e937b

3b

4a797781d

958

Pro

veIt

Bad

Rel

igio

nT

he

Pro

cess

Of

Be.

..P

un

k1f1

db

fdb

85ec

b964c3

d3175521b

bef

40

Pro

xim

aC

enta

uri

At

Th

eD

rive

-In

VA

YA

Pu

nk

b67fc

f3d

0e8

5a8d

f4a0b

7506b

c18b

d79

Sin

kO

rSw

imB

igw

igA

nIn

vit

ati

on

To

...

Pu

nk

507314ec

50b

048d

b7ac8

396b

dff

ba684

Som

eC

ame

Ru

nn

ing

Ban

eG

ive

Blo

od

Pu

nk

9d

52079

fed

ca997663b

2d

191d

4b

2ca

4e

Son

gfo

rL

un

aA

sT

all

As

Lio

ns

As

Tall

As

Lio

ns

Ind

ie2f9

6fe

e705d

1e1

5fd

c3247f0

7f5

4b

9f4

Str

ide

Ava

ilL

ive

at

the

Bott

o..

.P

un

kcd

4368e

b7afb

54d

54ad

f6222c0

782002

100

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Str

uck

Dow

nB

yM

eB

ane

ItA

llC

om

esD

own

...

Pu

nk

fb10289

7b

71404b

8ca

8d

f29b

6b

7a7643

Tak

enA

vail

On

eW

ren

chP

un

k33c4

4924f4

a199757d

fc8f6

795194a6d

Th

eB

ass

An

dT

he

Mo.

..A

tmos

ph

ere

God

Lov

esU

gly

Hip

-Hop

4ec

82a8b

ed38c6

2ca

59313a1d

53fd

005

Too

Mu

chT

oA

skB

adR

elig

ion

Gen

erato

rP

un

k9b

e103c

5ac6

41557a5b

8e1

85ec

ca788b

Try

ing

To

Fin

dA

Ba.

..A

tmos

ph

ere

Sev

en’s

Tra

vel

sH

ip-H

op

380cd

a82743629d

fdb

72905665ef

50b

5

Wic

ked

Gil

Ban

dO

fH

orse

sE

very

thin

gA

llT

h..

.In

die

020b

278

09b

4ab

87ea

22ee

50759568a9f

Dea

dG

irlf

rien

dC

ath

erin

eW

hee

lI

Want

toT

ou

chY

...

Alt

ern

ati

vee4

fa54b

58c7

a3238563b

5b

b220e5

f50b

Pan

icA

ttac

kA

tmos

ph

ere

You

Can

’tIm

agin

e...

Hip

-Hop

0f5

b379

ba96993ef

69ad

bfe

592f1

4f2

9

Th

eN

um

ber

On

eA

tmos

ph

ere

Sad

Clo

wn

Bad

Su

m..

.H

ip-H

op

5f9

854426ec

0c2

399340a9ab

74783f3

3

Th

eL

ast

To

Say

Atm

osp

her

eT

he

Fam

ily

Sig

nH

ip-H

op

7ce

feca

0e2

f73d

8e5

a17d

f1d

12fc

3152

Cri

sis

Bar

ney

Kes

sel

Poll

Win

ner

sT

hre

eJazz

865e8

0f2

a4239fb

b43f1

8cf

392fb

5ca

4

Lov

eis

Her

eto

Sta

yB

arn

eyK

esse

lK

esse

lP

lays

Sta

n..

.Jazz

8b

901ed

a9ad

a1d

c6b

7c7

c9a8c3

6a4c1

8

Mak

in’

Wh

oop

eeB

arn

eyK

esse

lM

usi

cT

oL

iste

nT

oJazz

40e9

54e0

bff

dce

9b

51120a8e3

02d

af0

0

Mon

sieu

rA

rman

dB

arn

eyK

esse

lP

oor

Bu

tter

fly

Jazz

ec117afa

8aff

2963ee

744484b

039ad

f8

Six

es&

Sev

ens

As

Tal

lA

sL

ion

sY

ou

Can

’tT

ake

It..

.In

die

a363e4

31590fd

873b

859876a56fd

8387

Tim

eR

emem

ber

edB

arn

eyK

esse

lL

et’s

Cook!

Jazz

12b

e2ea

42d

93ac9

4f0

565c4

caa02f9

b8

Th

eM

ajo

rL

eagu

esA

tmos

ph

ere

To

All

My

Fri

end

s...

Hip

-Hop

2ff

279a832b

e106af8

ced

5b

712668276

Alp

hra

zall

anA

llan

Hol

dsw

ort

hA

llN

ight

Wro

ng

Jazz

722eb

5ab

b14d

02c1

842010d

8219b

36d

a

Ble

eder

Alk

alin

eT

rio

Alk

ali

ne

Tri

oP

un

k1d

29b

345b

7ec

14a82b

e0eb

ad

db

1d

f58b

Bu

rnA

lkal

ine

Tri

oC

rim

son

Pu

nk

cfcf

c4c9

6782c7

4cc

ba01ab

a7ad

19b

c9

101

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

IA

mT

he

Law

Anth

rax

Am

on

gT

he

Liv

ing

Met

al

fce2

f471b

b3123ff

c214c0

8f5

d20f1

7d

IS

tay

Aw

ayA

lice

InC

hai

ns

Jar

Of

Fli

esA

lter

nati

veab

266fe

1c2

78e8

50707c7

55ec

f2b

3d

ff

Lea

dP

oiso

nin

gA

lkal

ine

Tri

oT

his

Ad

dic

tion

Pu

nk

719ac2

3ff

b162b

6fc

111501cb

85b

caaa

Mak

eM

eL

augh

Anth

rax

Sta

teO

fE

up

hori

aM

etal

0afa

c457b

2797c1

609e6

c50ee

0d

54510

No

Excu

ses

Ali

ceIn

Ch

ain

sM

TV

Un

plu

gged

Alt

ern

ati

ve20781943b

6289b

0f5

98ce

2613d

49c4

26

On

eH

un

dre

dSto

ries

Alk

alin

eT

rio

Good

Mou

rnin

gP

un

k5b

a8d

67666f6

572613577d

5a5582a997

Rai

nW

hen

ID

ieA

lice

InC

hai

ns

Dir

tA

lter

nati

ve94918f3

b6434b

2c9

f17b

9239c3

1b

5f6

3

San

Fra

nci

sco

Alk

alin

eT

rio

God

dam

nit

Pu

nk

baaf7

31a8d

4314eb

28fb

9c1

5e3

ec44b

9

Sea

Of

Sor

row

Ali

ceIn

Ch

ain

sF

ace

lift

Alt

ern

ati

vec3

b64cc

4e0

92e2

d7292af9

03c3

b67001

Sil

hou

ette

sS

ilh

oue.

..A

sT

all

As

Lio

ns

Lafc

ad

ioIn

die

305f9

6412d

2fe

d1c8

f4408ca

c145a9d

6

Slu

dge

Fac

tory

Ali

ceIn

Ch

ain

sA

lice

InC

hain

sA

lter

nati

vefe

1603c1

535ed

f112ec

d470c4

dc9

df6

f

Tak

eL

ots

Wit

hA

lcoh

olA

lkal

ine

Tri

oF

rom

Her

eto

Infi

...

Pu

nk

827d

6a2

4b

7066ae8

706f3

fd032a9cff

0

Wat

eron

the

Bra

in,.

..A

llan

Hol

dsw

ort

hR

oad

Gam

esJazz

dd

9c8

fe67b

65034b

6b

f2c8

82597c1

536

Win

dfa

llA

llE

lse

Fai

lsT

he

Ch

ase

Of

Gain

Pu

nk

dcf

c270

1b

a5399f3

93d

795f1

47e0

51a2

You

’ve

Got

So

Far

T..

.A

lkal

ine

Tri

oM

ayb

eI’

llC

atc

h..

.P

un

k30a58a0ab

3d

9050504fa

6c0

20352d

2b

b

Gli

tter

Cat

her

ine

Wh

eel

Ju

dy

Sta

rin

gA

tT

...

Alt

ern

ati

ve20812f9

5c5

841d

3b

9d

b232b

bb

8ed

3d

62

Roof

top

sA

lkal

ine

Tri

oA

lkali

ne

Tri

o,

Ho..

.P

un

k79a0b

3c1f1

fba642c1

ccd

fb813a682d

6

Th

isC

ould

Be

Lov

eA

lkal

ine

Tri

oD

am

nes

iaP

un

kc8

a624559cc

c4447cc

fd06310d

6b

6506

InV

ein

Alk

alin

eT

rio

Agony

An

dIr

ony

Pu

nk

6968b

5067f4

bc6

113125fc

6892d

16a9f

Wh

ite

Peo

ple

For

Pea

ceA

gain

stM

e!N

ewW

ave

Pu

nk

b23d

e3d

cde5

572f0

561cc

ffa4814a734

102

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Can

dy

InM

yC

avit

yA

gain

stT

omorr

ow’s

Sky

Th

eL

ost

Tap

esIn

die

fae6

e8d

de7

caa135865ea

acc

d429e6

03

Fai

rwea

ther

Fri

end

All

Mass

Ner

der

Pu

nk

523ff

ad

9536ef

a23c9

eca173aa245427

Fre

edom

Aga

inst

All

Au

thori

tyD

estr

oyW

hat

Des

t...

Pu

nk

377d

571

3c0

607128379479d

e31b

d6b

1c

Man

ha

De

Car

nav

alA

lD

iM

eola

,P

aco

D..

.T

he

Gu

itar

Tri

oJazz

2fd

b6d

4a3b

2f7

a025fc

48f0

1278a7632

Mon

eyA

lex

Skol

nic

kT

rio

Tra

nsf

orm

ati

on

Jazz

b4892fb

835b

c2c6

483832b

3d

9eb

78d

4b

Pen

Aga

inst

Tom

orr

ow’s

Sky

Ju

mp

Th

eH

edges

F..

.In

die

d5b

3ef

9065a0eb

20363ce

600d

bc5

6073

Sin

kF

lori

da

Sin

kA

gain

stM

e!A

sT

he

Ete

rnal

Co..

.P

un

k5f9

9b

415641b

76b

563165fa

4b

515d

b4d

IS

till

Lov

eY

ouJu

lie

Aga

inst

Me!

Rei

nve

nti

ng

Axl

Rose

Pu

nk

1f9

ed4c

dd

9a3255049ee

cb93b

71e0

ca0

Un

pro

tect

edS

exW

it..

.A

gain

stM

e!S

earc

hin

gfo

ra

F..

.P

un

k100d

3d

56ad

3017d

041d

b85336d

ffb

b3c

At

Ou

tE

xp

ense

Aga

inst

All

Au

thori

tyA

llF

all

Dow

nP

un

k677b

428

172cc

1d

257992b

7d

402f4

31a0

Bra

nd

New

Me,

Sam

e..

.A

Wil

hel

mS

crea

mM

ute

Pri

nt

Pu

nk

12095afb

bd

e72d

74271d

be2

2a71c2

153

Bro

wn

ieB

otto

mS

un

dae

AF

IA

nsw

erT

hat

An

dS

...

Pu

nk

224243fb

93cb

e83328619a244a8306a3

Dan

cin

gT

hro

ugh

Su

nd

ayA

FI

Sin

gT

he

Sorr

owP

un

k91a3ff

935795894fc

415139645914a83

Ad

van

ces

InM

od

ern

...

AF

IV

ery

Pro

ud

Of

Ya

Pu

nk

5d

98360

3ab

038b

067d

beb

4135e1

87c7

9

Clo

veS

mok

eC

ath

arsi

sA

FI

Bla

ckS

ail

sIn

Th

...

Pu

nk

5fe

d7b

77d

4ad

7cf

49cb

dd

1d

de7

cbb

d55

Hol

din

gB

ack

88F

inge

rsL

ou

ieB

ehin

dB

ars

Pu

nk

cda74f4

b621c4

93d

b305636ab

c9b

e541

InV

ino

Ver

itas

IIA

Wil

hel

mS

crea

mR

uin

erP

un

k3d

1d

1969ff

084f7

929a0f0

06b

98d

556b

3L

ibra

sA

Per

fect

Cir

cle

Mer

de

Nom

sM

etal

00f4

b93

acb

387c1

93f7

412d

e88fd

ead

a

You

rL

ove

AW

ilh

elm

Scr

eam

AW

ilh

elm

Scr

eam

Pu

nk

5a8f1

ec97d

36011897a664b

79b

f927b

e

Th

ese

Dea

dS

tree

tsA

Wil

hel

mS

crea

mC

are

erS

uic

ide

Pu

nk

451b

630

010a23d

50ae6

934fe

341d

8e8

e

103

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Wh

atIf

Th

ere’

sa

W..

.7

Sec

ond

sT

he

Cre

wP

un

kb

60151e

fecf

14d

68208747c6

4a994e9

3

Day

sO

fB

ein

gW

ild

...A

nd

You

Wil

lK

no..

.S

ou

rce

Tags

&C

od

esIn

die

2798d

33a7a45d

1aff

423ed

a55d

5ff

e27

Bla

ckM

etal

lic

(Pee

...

Cat

her

ine

Wh

eel

Cra

nk

(12”)

Alt

ern

ati

ve655c1

6fe

433ff

c9e3

4c0

de4

29fc

8949d

Com

eB

ack

Aga

inC

ath

erin

eW

hee

lC

ran

k(C

ass

ette

)A

lter

nati

ve1cf

37c8

774a3256b

eef6

bc7

a18ef

98c2

Su

ffoca

tion

Aga

inst

Me!

Wh

ite

Cro

sses

Pu

nk

dcb

490cc

29c3

cf0ae1

d89fa

8a787c9

c8

Wh

atis

Th

isT

hin

g..

.C

har

lie

Par

ker

Yard

bir

dS

uit

eD

i...

Jazz

aa30fc

140f0

7d

a45997ad

b0d

9c6

fd0a4

Par

tyO

ver

Her

eA

tmos

ph

ere

Sad

Clo

wn

Bad

Fal.

..H

ip-H

op

75b

2886

fe2690433a9089ec

651838399

Ma

Sol

itu

da

(Tim

Fr.

..C

ath

erin

eW

hee

lM

aS

oli

tud

a[D

isc

2]

Alt

ern

ati

ve9001e0

f8a0ed

40fe

95fb

e478b

768144d

Lit

tle

Mat

hY

ouA

tmos

ph

ere

Str

ictl

yL

eaka

ge

Hip

-Hop

29d

51b

8e1

04e8

dd

ec58e6

1b

31493f5

b5

Th

eyA

lway

sK

now

Atm

osp

her

eL

eak

At

Wil

lH

ip-H

op

e08cc

d9389a9b

b63651e3

182184a4585

Th

eS

how

erS

cen

eB

ran

dN

ewY

ou

rF

avori

teW

ea..

.P

un

k014e3

f25799fd

3c4

62ec

fa5f3

10d

672b

Don

’tW

ant

toK

now

...

Cat

her

ine

Wh

eel

30th

Cen

tury

Man

...

Alt

ern

ati

ve532ce

1aa28b

5b

81b

b5405cc

15c0

b270e

1597

Atm

osp

her

eO

verc

ast

!H

ip-H

op

db

d078

9afa

80b

09e3

ca95b

9f9

25aa9d

9

AS

ong

For

Th

eO

pti

...

Atr

eyu

Su

icid

eN

ote

san

d..

.M

etal

b61d

01a0fe

83393b

ee1b

82e8

51afd

baa

Arc

arse

nal

At

Th

eD

rive

-In

Rel

ati

on

ship

Of

C..

.P

un

k160392d

90d

6944e6

d71ed

362e3

a733cd

Bet

wee

nT

he

Lin

esA

tmos

ph

ere

Lu

cyF

ord

,T

he

At.

..H

ip-H

op

60f0

4cd

9171d

8083b

1988841d

d02d

46d

Lik

eT

he

Res

tO

fU

sA

tmos

ph

ere

Wh

enL

ife

Giv

esY

...

Hip

-Hop

15f9

03b

3b

29b

d729ad

ef85ff

14d

e70b

9

Au

tum

nL

eave

sA

ud

iocr

ush

So

You

Call

Th

ese.

..In

die

981197fc

f544b

0943f8

3e4

10c4

207e3

4

Fac

tory

Ban

dO

fH

orse

sIn

fin

ite

Arm

sIn

die

47c1

aa01b

b6a5fd

51ef

4d

7f3

d2d

1d

56c

Bre

akB

loss

omA

sT

all

As

Lio

ns

Blo

od

An

dA

ph

ori

sms

Ind

ie46e3

11e9

657898aa897fe

82b

ad

5474ad

104

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Bu

ryM

eA

uto

mat

ic7

Au

tom

ati

c7

Pu

nk

9e8

d2cc

1071ee

5939cb

4b

837eb

f3603b

Dee

pw

ood

Ava

ilO

ver

Th

eJam

esP

un

k17411b

20cd

5b

7cc

52ee

7b

a071ff

cb91b

Don

’tS

top

Atm

osp

her

eS

ad

Clo

wn

Bad

Win

...

Hip

-Hop

cfb

2165

523e4

5e8

140e4

676e0

a3346ea

”Fas

tO

ne”

Ava

ilO

ne

Wre

nch

Pu

nk

4c4

a4a913773d

2919ff

f25ec

05ab

7546

F**

*W

hat

You

Hea

rdB

ane

ItA

llC

om

esD

own

...

Pu

nk

a04a9494b

ad

f8d

813c5

8b

e5d

0f9

1f9

1b

Gen

erat

orB

adR

elig

ion

Gen

erato

rP

un

kd

7ce

2f6

dd

3eb

ef38a40a32699fe

f0629

Hel

loS

***t

yB

aysi

de

Bay

sid

eP

un

k44b

8e6

a032277e2

0f4

03767b

78e6

3d

1f

His

tory

Atm

osp

her

eS

even

’sT

ravel

sH

ip-H

op

0678958fc

ad

aac3

5d

e7e3

7ad

83b

f2c9

6

InP

iece

sB

ane

Hold

ing

Th

isM

om

ent

Pu

nk

a7d

9cb

c8b

6f0

53fa

a812c4

50465058d

6

Inco

mp

lete

Bad

Rel

igio

nS

tran

ger

Th

an

Fic

...

Pu

nk

fa4793b

69d

a4c8

18d

81647b

62837e0

3f

IsT

her

ea

Gh

ost

Ban

dof

Hor

ses

Cea

seto

Beg

inIn

die

10cc

1a79c6

6783d

ca59e7

edf8

a1c6

b53

Les

sO

ne

Atm

osp

her

eS

ad

Clo

wn

Bad

Sp

r...

Hip

-Hop

62e3

bfc

ca01075b

684222c6

9184cd

e6c

On

Th

eN

od

Ava

ilD

ixie

Pu

nk

cda63cc

bcf

499288810a53c8

741f3

b16

On

emos

ph

ere

Atm

osp

her

eG

od

Lov

esU

gly

Hip

-Hop

f1b

201e

6d

4d

d27943b

42e3

b9462b

7d

07

Ras

cuac

he

At

Th

eD

rive

-In

VA

YA

Pu

nk

a66d

7d

10b

54fb

e6fc

995716b

04ac9

60c

Rev

elat

ion

sA

ud

iosl

ave

Rev

elati

on

sA

lter

nati

ved

12ee

a2d

15fc

666033099d

874b

086326

Sim

ple

Son

gA

vail

4A

MF

rid

ayP

un

k3ca

9e3

4877c2

e16759e5

2a5aa1a5cb

17

Sou

thB

oun

d95

Ava

ilL

ive

at

the

Bott

o..

.P

un

kca

f18aeb

33a2b

0b

4e7

6ace

f97d

f9ad

d4

Sp

eech

less

Ban

eG

ive

Blo

od

Pu

nk

373d

250

9025d

b1ee

3fa

fc007b

1e2

671b

Sta

bC

ity

As

Tal

lA

sL

ion

sA

sT

all

As

Lio

ns

Ind

ied

f17d

a444b

6aab

6cc

7173704b

af4

3872

105

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Su

per

son

icB

adR

elig

ion

Th

eP

roce

ssO

fB

e...

Pu

nk

ef66827396d

a576f1

9f2

d823e2

57f5

9f

Th

eF

irst

Son

gB

and

Of

Hor

ses

Eve

ryth

ing

All

Th

...

Ind

ieb

88f0

2f59ef

df3

12f0

e7b

e6856b

be6

58

Th

eS

hin

ing

Bad

lyD

raw

nB

oyT

he

Hou

rO

fB

ewil

...

Ind

ieed

17ed

7e9

388680c8

3064126ec

b47a43

Th

ese

Day

sB

adA

stro

nau

tH

ou

ston

:W

eH

ave

...

Pu

nk

dd

bb

3b

853b

8145699571e4

56d

0b

51d

7d

You

Are

(Th

eG

over

n..

.B

adR

elig

ion

Su

ffer

Pu

nk

8d

46566

665324a1d

7916044376fb

baa5

Su

nsh

ine

Atm

osp

her

eS

ad

Clo

wn

Bad

Su

m..

.H

ip-H

op

99e0

8b

d68fe

8fc

6571b

a35f3

08fc

c056

Th

eA

rriv

alA

tmos

ph

ere

You

Can

’tIm

agin

e...

Hip

-Hop

dee

e69e

23211e9

1e2

d3a8a5234aa5a1e

My

Key

Atm

osp

her

eT

he

Fam

ily

Sig

nH

ip-H

op

4d

d3c8

8f0

87b

47338cc

f750c3

27568e8

Ch

eerf

ul

Lit

tle

Ear

ful

Bar

ney

Kes

sel

Mu

sic

To

Lis

ten

To

Jazz

076f5

d0ed

0473834ac9

3b

b912a2e5

bc4

Cir

cles

As

Tal

lA

sL

ion

sY

ou

Can

’tT

ake

It..

.In

die

a42b

d09b

8a59074ff

ccd

264fb

334f7

6f

Dea

rly

Bel

oved

Bar

ney

Kes

sel

Poor

Bu

tter

fly

Jazz

eded

0e6

f16f5

7019b

79c2

a90cb

18c8

53

Let

’sC

ook

!B

arn

eyK

esse

lL

et’s

Cook!

Jazz

69b

18e7

102787ee

f6d

8f5

6338008b

b79

Sof

tW

ind

sB

arn

eyK

esse

lP

oll

Win

ner

sT

hre

eJazz

cfd

6873

b55fd

409a80f7

caa7850f6

c57

Sp

eak

Low

Bar

ney

Kes

sel

Kes

sel

Pla

ys

Sta

n..

.Jazz

21d

98b

f643cf

5ca

91e2

7c8

08c9

39fe

f2

Unti

lT

he

Nip

ple

sG

one

Atm

osp

her

eT

oA

llM

yF

rien

ds.

..H

ip-H

op

0d

25724

f531b

bb

a15b

4b

c2c7

587b

4f5

e

96H

eart

bea

tsA

sT

all

As

Lio

ns

Lafc

ad

ioIn

die

2fc

77b

7ae2

a81cf

04c7

3140d

8c2

fb8af

Anth

emA

llE

lse

Fai

lsT

he

Ch

ase

Of

Gain

Pu

nk

dea

6064

e0c2

25e7

59a5f3

1cf

db

78a2b

7

Bro

ther

Ali

ceIn

Ch

ain

sM

TV

Un

plu

gged

Alt

ern

ati

vea4c5

c2081fc

0fb

48e6

e669b

16b

289acf

Bru

shA

way

Ali

ceIn

Ch

ain

sA

lice

InC

hain

sA

lter

nati

ve4636a3b

d01b

7fe

8d

fa0a9e4

21a981d

6b

Cau

ght

InA

Mos

hA

nth

rax

Am

on

gT

he

Liv

ing

Met

al

52ae3

b75b

ea43d

c92eb

b4b

61e3

a2ca

0d

106

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Cop

Alk

alin

eT

rio

God

dam

nit

Pu

nk

297c7

a70182f7

6d

f112fc

f54a3a46f2

8

Dam

Th

atR

iver

Ali

ceIn

Ch

ain

sD

irt

Alt

ern

ati

ved

07b

b2104b

ae9

a0842793fe

376475623

Din

e,D

ine

My

Dar

lin

gA

lkal

ine

Tri

oT

his

Ad

dic

tion

Pu

nk

9e6

25766ce

14918a97b

c968f0

ea97c6

4

I’ll

Get

Th

ere

All

Mass

Ner

der

Pu

nk

58267e2

f9b

f2cb

f5b

395869ee

aff

4e6

3

Mad

amM

eA

lkal

ine

Tri

oM

ayb

eI’

llC

atc

h..

.P

un

kd

272554

ef8aad

db

7f8

9d

5d

3065b

6ca

4c

Man

InT

he

Box

Ali

ceIn

Ch

ain

sF

ace

lift

Alt

ern

ati

ve1b

e84cd

21857fb

eeec

cfb

31d

dae7

62b

6

Mr.

Ch

ain

saw

Alk

alin

eT

rio

Fro

mH

ere

toIn

fi..

.P

un

kfb

d1a7892a2624a56ed

260ec

6662c8

21

Nu

tsh

ell

Ali

ceIn

Ch

ain

sJar

Of

Fli

esA

lter

nati

ve5156fc

167c5

0301b

2a72f4

ff96014c5

3

Ou

tO

fS

ight,

Ou

tO

...

Anth

rax

Sta

teO

fE

up

hori

aM

etal

520a24f8

29b

d3cc

d27a1afe

144b

ce833

Roa

dG

ames

All

anH

old

swort

hR

oad

Gam

esJazz

6724cb

8fb

0a33b

8e6

8ac1

de0

f9418ce

2

Th

eP

oiso

nA

lkal

ine

Tri

oC

rim

son

Pu

nk

3b

988c3

54ea

505c8

63d

6f1

b3b

d45b

2d

f

Th

eT

hin

gsY

ouse

eA

llan

Hol

dsw

ort

hA

llN

ight

Wro

ng

Jazz

2ac2

594a3429a065fd

a2b

642acc

5f3

80

Th

isIs

Get

tin

gO

ve..

.A

lkal

ine

Tri

oA

lkali

ne

Tri

oP

un

k2700b

a179b

309fe

1e2

ba94cf

be4

4b

45d

We’

veH

adE

nou

ghA

lkal

ine

Tri

oG

ood

Mou

rnin

gP

un

k9d

bf6

3c2

d4253b

7b

3f2

fa86757c9

b1b

b

Nos

eO

ver

Tai

lA

lkal

ine

Tri

oD

am

nes

iaP

un

kea

fb4e7

d24f4

ff1b

dcb

652fb

0d

859d

44

Wh

ile

You

’re

Wai

tin

gA

lkal

ine

Tri

oA

lkali

ne

Tri

o,

Ho..

.P

un

kcc

52e8

2a4631b

58154e8

858d

9125e3

29

Hel

pM

eA

lkal

ine

Tri

oA

gony

An

dIr

ony

Pu

nk

7ed

ae0

48961160628051411ff

905b

8f3

Th

rash

Un

real

Aga

inst

Me!

New

Wav

eP

un

k6a7a523876a194fb

27e4

2d

9b

7e6

f6c9

0

Bli

nd

Fol

ds

and

Pad

...

Aga

inst

Tom

orr

ow’s

Sky

Th

eL

ost

Tap

esIn

die

8aec

ab

b9e0

faaee

1e6

0817557401d

0d

0

Con

dit

ion

ing

Aga

inst

All

Au

thori

tyD

estr

oyW

hat

Des

t...

Pu

nk

29b

c07a

e80e3

c1d

6e2

c6840ef

c879774

107

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Fea

rO

fF

lyin

gA

lex

Skol

nic

kT

rio

Tra

nsf

orm

ati

on

Jazz

5e1

1e9

d4c1

acd

6e7

b8aec

596c7

cd13d

6

Mid

sum

mer

Nig

ht

Al

Di

Meo

la,

Paco

D..

.T

he

Gu

itar

Tri

oJazz

2f5

ff4a0f4

f060ae9

79b

50f8

044ef

8e3

Mu

tiny

onth

eE

lecr

...

Aga

inst

Me!

As

Th

eE

tern

al

Co..

.P

un

kce

bb

9d

7165b

70fa

9d

2b

4c6

a3b

d21ed

82

Poi

son

Tes

ter

Aga

inst

Tom

orr

ow’s

Sky

Ju

mp

Th

eH

edges

F..

.In

die

3529f2

29c4

a62f8

ee11e6

6d

c9c1

16301

Ju

stin

Aga

inst

Me!

Sea

rch

ing

for

aF

...

Pu

nk

363ac4

23ef

e72af8

c2737233ea

f36883

We

Lau

ghat

Dan

ger

...

Aga

inst

Me!

Rei

nve

nti

ng

Axl

Rose

Pu

nk

56ef

99a3b

e326b

78e0

ab

82a96d

49c1

33

IW

ann

aG

etA

Moh

aw..

.A

FI

An

swer

Th

at

An

dS

...

Pu

nk

cc19d

2979f6

ecb

be0

2b

0d

923a38a6e7

b

Kee

pT

ryin

gA

gain

stA

llA

uth

ori

tyA

llF

all

Dow

nP

un

kc8

c706401e6

847fd

41c5

1a5c0

8f6

b5cf

Mal

leu

sM

alefi

caru

mA

FI

Bla

ckS

ail

sIn

Th

...

Pu

nk

8a24a1314ec

8a4d

e11c2

268a602ac3

4e

Sil

ver

An

dC

old

AF

IS

ing

Th

eS

orr

owP

un

k11ef

0a529533f5

29b

c32f5

381e9

88b

b7

Tot

alim

mor

tal

AF

IA

llH

all

ow’s

E.P

.P

un

k06579371f3

6fa

504ea

85b

8992a7c7

442

Wil

liam

sB

lake

Ove

r...

AW

ilh

elm

Scr

eam

Mu

teP

rint

Pu

nk

5169725b

bc9

a9b

b506fc

d5c5

99a6f5

d4

God

Lov

esa

Lia

rA

Wil

hel

mS

crea

mR

uin

erP

un

k6f2

a4f0

e65b

a70169588971e1

ae3

9fd

f

Jaw

s3,

Peo

ple

0A

Wil

hel

mS

crea

mC

are

erS

uic

ide

Pu

nk

0939170876214ab

753e1

a7c3

108a71a9

Per

fect

Fit

AF

IV

ery

Pro

ud

Of

Ya

Pu

nk

76d

de3

e86e0

811231ce

bb

e49492242e8

Sm

art

En

ough

To

Run

88F

inge

rsL

ou

ieB

ehin

dB

ars

Pu

nk

1e4

82cd

4504e4

526c9

b34893d

c771446

Ore

stes

AP

erfe

ctC

ircl

eM

erd

eN

om

sM

etal

5b

bd

795f4

1d

a562d

69625a19ec

94f3

3b

Skid

Rock

AW

ilh

elm

Scr

eam

AW

ilh

elm

Scr

eam

Pu

nk

c5a604536c5

6141a51ea

3d

65922d

46c6

Gir

lsB

east

ieB

oys

Lic

ense

dT

oIl

lH

ip-H

op

9d

01d

2a75ae6

ac9

ad

5c2

2fe

4f6

739d

4c

You

Los

e7

Sec

ond

sT

he

Cre

wP

un

k189728103b

a8837fc

950ad

668f2

8c3

33

108

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Mon

soon

...A

nd

You

Wil

lK

no..

.S

ou

rce

Tags

&C

od

esIn

die

54567a6cf

5829154674f9

1a6e9

6714b

b

Pey

ote

Atm

osp

her

eS

ad

Clo

wn

Bad

Fal.

..H

ip-H

op

956af7

1450300d

af0

43c7

6587b

079850

Bec

ause

Of

Th

eS

ham

eA

gain

stM

e!W

hit

eC

ross

esP

un

k3a73b

23f3

f6d

073724cd

54eb

13b

4285c

C’m

onA

tmos

ph

ere

Lea

kA

tW

ill

Hip

-Hop

e4783543f0

63640691d

728d

b47108571

Ygm

Atm

osp

her

eS

tric

tly

Lea

kage

Hip

-Hop

687f5

b59b

0fc

0ed

0d

879b

e292f4

31ac4

Am

ong

Th

eL

ivin

gA

nth

rax

Am

on

gT

he

Liv

ing

Met

al

ce7b

f99b

1d

0a96951b

91b

8b

f7135d

45b

Be

All

,E

nd

All

Anth

rax

Sta

teO

fE

up

hori

aM

etal

e63e5

2584945a19984f2

1828e6

e1d

fd0

Cri

nge

Alk

alin

eT

rio

God

dam

nit

Pu

nk

9fa

f97d

49f5

f144ee

df2

4b

42eb

c16fd

c

Div

isio

nA

llE

lse

Fai

lsT

he

Ch

ase

Of

Gain

Pu

nk

edd

e840b

88e1

9ea

cab

eda9c7

6e7

7fe

66

Good

bye

For

ever

Alk

alin

eT

rio

Alk

ali

ne

Tri

oP

un

k1974d

5407d

ebb

0a1b

d78f7

b0589ec

28d

Gri

nd

Ali

ceIn

Ch

ain

sA

lice

InC

hain

sA

lter

nati

ve761fd

5388cd

bf4

b221a4c6

0ed

40d

b0c9

Kee

p’E

mC

omin

gA

lkal

ine

Tri

oM

ayb

eI’

llC

atc

h..

.P

un

ka0eb

d3d

f9e0

83d

33acf

d95e2

65b

089d

b

Lan

yard

Loop

All

anH

old

swort

hA

llN

ight

Wro

ng

Jazz

0ac0

0cb

a14e3

cba0aad

3d

f1d

b47188ca

Nu

tsh

ell

Ali

ceIn

Ch

ain

sM

TV

Un

plu

gged

Alt

ern

ati

ve444b

538

4a3e5

25a51cc

a0b

791ec

04756

Pri

vate

Eye

Alk

alin

eT

rio

Fro

mH

ere

toIn

fi..

.P

un

k8b

cb0c2

8f0

a0f3

33b

846b

b44028643f1

Rot

ten

Ap

ple

Ali

ceIn

Ch

ain

sJar

Of

Fli

esA

lter

nati

ve1e5

65fb

418975ea

391b

a022374a6e5

9b

Th

emB

ones

Ali

ceIn

Ch

ain

sD

irt

Alt

ern

ati

ve13e0

1175265149b

58317ec

b4c1

53f5

ec

Th

isA

dd

icti

onA

lkal

ine

Tri

oT

his

Ad

dic

tion

Pu

nk

18857886c1

8d

f863b

2b

5d

f049b

54cf

3e

Th

isC

ould

Be

Lov

eA

lkal

ine

Tri

oG

ood

Mou

rnin

gP

un

kd

92f2

c30ef

6d

c2e4

82f0

02273aa3fe

44

Th

ree

Sh

eets

toth

e...

All

anH

old

swort

hR

oad

Gam

esJazz

92904a3d

ab

52ae4

af0

541546ef

bee

d95

109

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Tim

eT

oW

aste

Alk

alin

eT

rio

Cri

mso

nP

un

k549ce

3c8

365a908443478f0

e8b

ba1293

Was

teB

igw

igA

nIn

vit

ati

on

To

...

Pu

nk

bb

58d

847e7

efc1

62fe

e8cc

7f4

2ce

b8fc

We

Die

You

ng

Ali

ceIn

Ch

ain

sF

ace

lift

Alt

ern

ati

ved

1d

332e8

675375e9

7e6

c38f0

6601185c

Wor

ld’s

On

Her

oin

All

Mass

Ner

der

Pu

nk

4c1

ee1d

96d

416e7

0e6

d2e7

266199b

5a4

Cal

lin

gA

llS

kele

ton

sA

lkal

ine

Tri

oD

am

nes

iaP

un

k765e4

557a749e6

aed

ae5

6c5

a38845035

Qu

een

ofP

ain

Alk

alin

eT

rio

Alk

ali

ne

Tri

o,

Ho..

.P

un

ke4

a82e5

77d

bab

3a679845a689e1

e0f8

1

Cal

lin

gA

llS

kele

ton

sA

lkal

ine

Tri

oA

gony

An

dIr

ony

Pu

nk

97f1

91fb

9736eb

dd

ece8

c69081c1

b6ff

Up

Th

eC

uts

Aga

inst

Me!

New

Wav

eP

un

kf9

d7018

88e6

e49ee

a336b

062d

e8b

67f7

Bey

ond

the

Mir

age

Al

Di

Meo

la,

Paco

D..

.T

he

Gu

itar

Tri

oJazz

640b

35c

9afe

fc85d

b6aa47301d

dd

1f2

7

Cli

che

Gu

evar

aA

gain

stM

e!A

sT

he

Ete

rnal

Co..

.P

un

k0765d

6939b

ed976e3

4c6

2a979c1

af4

10

Ele

ctri

cE

ye

Ale

xS

kol

nic

kT

rio

Tra

nsf

orm

ati

on

Jazz

415d

59ff

d07d

75781342463798d

65b

a0

No

Rea

son

Aga

inst

All

Au

thori

tyD

estr

oyW

hat

Des

t...

Pu

nk

a04c4

669d

055ed

b2e6

8d

27d

83f6

b217e

San

sP

lan

ZA

gain

stT

omorr

ow’s

Sky

Th

eL

ost

Tap

esIn

die

f015cb

9cf

50682c5

d517187d

f6e6

eef4

Th

row

ing

Bro

ken

Sh

apes

Aga

inst

Tom

orr

ow’s

Sky

Ju

mp

Th

eH

edges

F..

.In

die

1f8

d9ed

3d

b93ac4

14d

5a9ae1

1b

9eb

e3a

Med

iocr

ity

Get

sY

ou..

.A

gain

stM

e!S

earc

hin

gfo

ra

F..

.P

un

k2eb

6631

6212d

41561642fa

66933a0ef

5

Th

eP

olit

ics

ofS

ta..

.A

gain

stM

e!R

einve

nti

ng

Axl

Rose

Pu

nk

768b

7ab

b853b

e6f8

3277e1

59707b

c934

An

chor

En

dA

Wil

hel

mS

crea

mM

ute

Pri

nt

Pu

nk

cef5

0cb

8ae0

00b

f79b

f4e7

2d

a357f1

e9

Ble

edB

lack

AF

IS

ing

Th

eS

orr

owP

un

k8373a879f3

7d

ab

a3ce

d662a1aae8

744f

Exsa

ngu

inat

ion

AF

IB

lack

Sail

sIn

Th

...

Pu

nk

7a66fc

b247d

64f4

66b

694d

6d

ca4d

454a

Ju

stifi

cati

onA

gain

stA

llA

uth

ori

tyA

llF

all

Dow

nP

un

k90d

e01e

d01733cd

b9c1

71597d

0c1

5439

110

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Th

eB

oyW

ho

Des

troy

...

AF

IA

llH

all

ow’s

E.P

.P

un

k03c3

560b

0a45c0

7f4

8cc

247ec

44f1

ec8

Yu

rfR

end

enm

ein

AF

IA

nsw

erT

hat

An

dS

...

Pu

nk

87e5

41a0608a46ee

05aa7f1

3fe

81e6

23

Cu

ltS

tatu

sA

FI

Ver

yP

rou

dO

fY

aP

un

k96d

baac4

a869cc

43d

1d

48d

f5912c4

006

Die

Wh

ile

We

reY

oun

gA

Wil

hel

mS

crea

mC

are

erS

uic

ide

Pu

nk

863e6

48b

77474c9

fad

80d

59376169b

a0

Had

My

Ch

ance

88F

inge

rsL

ou

ieB

ehin

dB

ars

Pu

nk

1fe

7c3

c289574d

d76605b

8357d

44d

0e4

Th

eS

oft

Sel

lA

Wil

hel

mS

crea

mR

uin

erP

un

k350297477c9

24a7f8

a738568742b

8248

Ju

dit

hA

Per

fect

Cir

cle

Mer

de

Nom

sM

etal

55e3

74e4

7f4

a7fb

2ca

5c6

4a41e4

3c9

fb

Bu

llet

pro

ofT

iger

AW

ilh

elm

Scr

eam

AW

ilh

elm

Scr

eam

Pu

nk

6e6

f81608261b

9c8

ef1d

4f4

6552910d

e

Str

aigh

tO

n7

Sec

ond

sT

he

Cre

wP

un

k7c6

fd7b

6e1

88354c5

7514fa

de3

e9108d

Hea

rtIn

Th

eH

and

O..

...

.An

dY

ouW

ill

Kn

o..

.S

ou

rce

Tags

&C

od

esIn

die

6d

e6b

235b

809ab

cd1ef

e4ec

44e4

3d

822

IW

asA

Tee

nag

eA

na.

..A

gain

stM

e!W

hit

eC

ross

esP

un

kd

fe06b

3f1

24f8

93d

83d

d82a0715001ef

New

Wav

eA

gain

stM

e!N

ewW

ave

Pu

nk

7b

21e4

bff

f344b

62d

602b

f2c9

d5d

ccc4

Eag

erA

gain

stT

omorr

ow’s

Sky

Ju

mp

Th

eH

edges

F..

.In

die

601fa

b833fc

588f3

f4544e3

143d

cab

14

La

Est

iba

Al

Di

Meo

la,

Paco

D..

.T

he

Gu

itar

Tri

oJazz

48d

0fd

f5fe

cbb

3f1

ad

fb8ee

86f8

45795

Lif

esty

leof

Reb

elli

onA

gain

stA

llA

uth

ori

tyD

estr

oyW

hat

Des

t...

Pu

nk

38857d

4b

ed8d

552e2

86744f0

2a4f5

eea

Lu

llab

yeL

eagu

eA

gain

stT

omorr

ow’s

Sky

Th

eL

ost

Tap

esIn

die

f098b

b40ae6

fcfa

c99233005b

3b

1ad

c6

T.S

.R.

Aga

inst

Me!

As

Th

eE

tern

al

Co..

.P

un

ka7f4

c5325c0

66d

8103328a076f4

f8636

Tra

nsf

orm

atio

nA

lex

Skol

nic

kT

rio

Tra

nsf

orm

ati

on

Jazz

c2756fe

99aacb

1546799aaff

4f0

01ed

6

Mia

mi

Aga

inst

Me!

Sea

rch

ing

for

aF

...

Pu

nk

9c8

c9f8

cc03c6

8ce

eda29991f3

3ae8

77

Pin

tsof

Gu

inn

ess

M..

.A

gain

stM

e!R

einve

nti

ng

Axl

Rose

Pu

nk

728c8

b8b

21f0

9130b

b39d

7d

6e3

483356

111

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

12:0

0A

MA

gain

stA

llA

uth

ori

tyA

llF

all

Dow

nP

un

kb

7a9ae6

922d

e955952f3

3312b

e840e7

c

Fam

ous

Fri

end

sA

nd

...

AW

ilh

elm

Scr

eam

Mu

teP

rint

Pu

nk

f77658c6

edd

12ad

bfc

bed

92ae2

fdd

fb6

Hal

f-em

pty

Bot

tle

AF

IA

nsw

erT

hat

An

dS

...

Pu

nk

16f5

4d

678c8

e4f2

e7973f1

3b

70c0

c2e1

Hal

low

een

AF

IA

llH

all

ow’s

E.P

.P

un

k20490af2

9384a6d

21988e0

356504fb

e5

Kil

lin

gIt

AW

ilh

elm

Scr

eam

Ru

iner

Pu

nk

134422285eb

d2d

cc8a9f0

26fb

886f8

cf

Por

phyri

aA

FI

Bla

ckS

ail

sIn

Th

...

Pu

nk

4fc

f656e5

7eb

8589d

b03a4b

77880a442

Th

eL

eavin

gS

ong

Pt.

2A

FI

Sin

gT

he

Sorr

owP

un

kca

0996b

052e9

f669a13f9

7b

929f9

4321

Ord

erA

vail

Liv

eat

the

Bott

o..

.P

un

ka2b

5a9f

08f0

0c0

5f1

21a7400b

797e8

89

Ou

trig

ht

Lie

s88

Fin

gers

Lou

ieB

ehin

dB

ars

Pu

nk

f24799443b

b1ec

da9e2

8b

9944fb

15662

Th

eH

orse

AW

ilh

elm

Scr

eam

Care

erS

uic

ide

Pu

nk

e3738417a76321a8900a5f3

3f7

59b

958

Wak

e-u

pC

all

AF

IV

ery

Pro

ud

Of

Ya

Pu

nk

80cd

f471b

83cf

199b

e5009d

85d

c88f2

e

Ros

eA

Per

fect

Cir

cle

Mer

de

Nom

sM

etal

fa66a9fb

a293f3

6992e7

8ed

7cf

3b

45d

6

Fu

nT

ime

AW

ilh

elm

Scr

eam

AW

ilh

elm

Scr

eam

Pu

nk

674d

19d

33b

5a84ca

c07c6

29b

4634b

167

Th

isIs

the

An

gry,

...

7S

econ

ds

Th

eC

rew

Pu

nk

d24f2

7c3e8

b8b

337d

bfe

7151b

723366a

How

Nea

r,H

owF

ar..

.An

dY

ouW

ill

Kn

o..

.S

ou

rce

Tags

&C

od

esIn

die

0193ae3

ced

25974b

99708ee

19599785c

Iron

Man

Ali

ceIn

Ch

ain

sD

irt

Alt

ern

ati

vec1

a51d

5f9

33ee

609f6

f6c9

082c2

b1acb

Wh

ite

Cro

sses

Aga

inst

Me!

Wh

ite

Cro

sses

Pu

nk

9d

be6

d1ff

570b

8d

3c5

8057175e0

e5273

All

Fal

lD

own

Aga

inst

All

Au

thori

tyA

llF

all

Dow

nP

un

k2a823a3b

7a0f1

9d

4845cd

a728c6

5016f

Fal

lC

hil

dre

nA

FI

All

Hall

ow’s

E.P

.P

un

k6cc

151e6

b2d

8967e9

8a6a29b

66b

256ca

He

Wh

oL

augh

sL

ast

AF

IV

ery

Pro

ud

Of

Ya

Pu

nk

44e5

bcd

b65a89026a9d

160300b

386b

5a

112

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Mis

eria

Can

tare

–Th

...

AF

IS

ing

Th

eS

orr

owP

un

k755446b

4d

499523d

9ee

41e8

ef6090ac4

Mu

teP

rint

AW

ilh

elm

Scr

eam

Mu

teP

rint

Pu

nk

76b

af8

7b

c8eb

c4ee

29af7

4d

a28f5

a019

Str

engt

hT

hro

ugh

Wo.

..A

FI

Bla

ckS

ail

sIn

Th

...

Pu

nk

98c0

8b

644b

2259d

ad

8ad

3afc

74b

99663

Th

eK

ing

isD

ead

AW

ilh

elm

Scr

eam

Ru

iner

Pu

nk

4a0b

5d

f76f9

865d

8d

c435b

a86150a69d

Tw

oO

fA

Kin

dA

FI

An

swer

Th

at

An

dS

...

Pu

nk

911cd

bc3

4949d

e445a1172016e3

58fb

4

5T

o9

AW

ilh

elm

Scr

eam

Care

erS

uic

ide

Pu

nk

c3f2

d10

933e6

873ea

80d

d5ef

54089237

Exp

lan

atio

n88

Fin

gers

Lou

ieB

ehin

dB

ars

Pu

nk

79b

ac1

7176f3

5c6

0b

30a687cd

9512f0

e

Mag

dal

ena

AP

erfe

ctC

ircl

eM

erd

eN

om

sM

etal

08061f7

d017d

ac9

a95786f5

760a49a79

Eve

ryG

reat

Sto

ryH

...

AW

ilh

elm

Scr

eam

AW

ilh

elm

Scr

eam

Pu

nk

0296c2

052c0

bf9

784d

933d

61ab

e68f0

e

Bau

del

aire

...A

nd

You

Wil

lK

no..

.S

ou

rce

Tags

&C

od

esIn

die

a3735c4

4f9

edb

69993d

7921961a93a70

Lif

eO

nT

he

Roa

dA

llM

ass

Ner

der

Pu

nk

0ef

1b

0174d

aa8ad

3e5

a6c1

078f7

20ad

5

Not

Ju

stB

oys

Fu

n7

Sec

ond

sT

he

Cre

wP

un

k77ef

59d

a82a5fc

d49191a469ab

1874d

3

Rep

arat

ion

88F

inge

rsL

ou

ie88

Fin

ger

sL

ou

ie..

.P

un

k920b

a30

f32fe

71e8

6fa

86b

2375f0

cb65

Har

k!

the

Her

ald

An

...

Bin

gC

rosb

yF

rank

...

Ch

rist

mas

Jazz

96e1

4345b

837229b

f9e4

8f2

21eb

dd

439

30S

ec.

Son

gA

gain

stA

llA

uth

ori

tyD

estr

oyW

hat

Des

t...

Pu

nk

d932fc

589448e4

11096cc

94b

9843b

3a5

Pen

tU

p88

Fin

gers

Lou

ieB

ehin

dB

ars

Pu

nk

f956b

4e3144b

241d

20603e9

a8c2

f6574

Th

eH

ollo

wA

Per

fect

Cir

cle

Mer

de

Nom

sM

etal

139ac4

259aafc

07c6

d8a6a435d

9167e8

Au

stra

lias

AW

ilh

elm

Scr

eam

AW

ilh

elm

Scr

eam

Pu

nk

2f8

787c1

efae9

5f6

675d

cbc7

7178b

324

An

oth

erM

orn

ing

Sto

ner

...A

nd

You

Wil

lK

no..

.S

ou

rce

Tags

&C

od

esIn

die

b32aaf6

384d

ceb

181d

2ee

851acd

fa86d

Defi

nit

eC

hoi

ce7

Sec

ond

sT

he

Cre

wP

un

kb

4e1

23a

85d

d40f1

d2056b

51a5921b

496

113

Tab

leB

.1–FullEvaluationDatasetContinued

Song

Artist

Album

Genre

MD5

Slo

wC

hor

us

Ove

rlap

88F

inge

rsL

ou

ie88

Fin

ger

sL

ou

ie..

.P

un

kf2

cbc4

bf2

936c7

31075f7

a9cf

8b

7f3

18

Car

eer

Su

icid

eA

Wil

hel

mS

crea

mC

are

erS

uic

ide

Pu

nk

f3a3e0

9897d

fb159073d

f1d

5d

4b

a9e2

4

Zer

oF

risc

oB

raid

Fra

nkie

Wel

fare

B..

.In

die

690a79d

fcef

0372f9

43d

c4b

013009868

Her

e’s

You

rW

arn

ing

7S

econ

ds

Th

eC

rew

Pu

nk

24f9

c12d

fb84f8

b94718f4

71120d

730c

IW

ipe

My

A**

Wit

h..

.A

Wil

hel

mS

crea

mC

are

erS

uic

ide

Pu

nk

51ee

4f0

043069e3

f41c3

cb52b

c6b

1200

ItW

asT

her

eT

hat

I...

...A

nd

You

Wil

lK

no..

.S

ou

rce

Tags

&C

od

esIn

die

27707e7

d00b

d2036e8

548c2

698f5

11b

7

Ou

tT

her

e88

Fin

gers

Lou

ie88

Fin

ger

sL

ou

ie..

.P

un

k505626e5

0b

828fc

415e7

a809846c3

a88

Fil

e13

AF

IV

ery

Pro

ud

Of

Ya

Pu

nk

3262b

f7036fe

a610ca

b9d

c2aed

5af8

1b

Hom

age

...A

nd

You

Wil

lK

no..

.S

ou

rce

Tags

&C

od

esIn

die

4b

a200e

93b

3ed

7b

2a690aa81b

8fe

e497

Cle

nch

edF

ists

,B

la..

.7

Sec

ond

sT

he

Cre

wP

un

k91e5

14b

8af4

59fd

7e1

e05f0

51f8

37a6a

Nar

rati

veO

fS

oul

A..

.A

FI

Bla

ckS

ail

sIn

Th

...

Pu

nk

a7b

0790

c1e0

5012d

aa21c6

feca

b12142

Th

eP

ool

AW

ilh

elm

Scr

eam

Ru

iner

Pu

nk

86eff

07e2

414963c4

356d

826500b

772c

114

References

[1] Gul Abdulnabi Agha. Actors: a model of concurrent computation in distributedsystems. MIT Press, Cambridge, MA, 1985.

[2] Akka. http://akka.io/. Last visited 11/18/2013.

[3] Krste Asanovic, Ras Bodik, Bryan Christopher Catanzaro, Joseph James Gebis,Parry Husbands, Kurt Keutzer, David A. Patterson, William Lester Plishker,John Shalf, Samuel Webb Williams, and Katherine A. Yelick. The land-scape of parallel computing research: A view from berkeley. Technical ReportUCB/EECS-2006-183, EECS Department, University of California, Berkeley,Dec 2006.

[4] Sebastian Bock, Florian Krebs, and Markus Schedl. Evaluating the online capa-bilities of onset detection methods. In ISMIR, pages 49–54, 2012.

[5] Stuart Bray and George Tzanetakis. Distributed audio feature extraction formusic. In ISMIR, pages 434–437, 2005.

[6] Judith C Brown and Miller S Puckette. An efficient algorithm for the calculationof a constant q transform. The Journal of the Acoustical Society of America,92:2698, 1992.

[7] Jamie Bullock and UCEB Conservatoire. Libxtract: A lightweight library foraudio feature extraction. In Proceedings of the International Computer MusicConference, volume 43, 2007.

[8] Chris Cannam, Michael O. Jewell, Christophe Rhodes, Mark Sandler, and Markd’Inverno. Linked data and you: Bringing music research software into thesemantic web. Journal of New Music Research, 39(4):313–325, 2010.

[9] Akka Clustering. http://doc.akka.io/docs/akka/2.2.1/common/cluster.

html. Last visited 02/10/2014.

[10] John Cocke. Global common subexpression elimination. ACM Sigplan Notices,5(7):20–24, 1970.

[11] BBC Vamp Plugin Collection. http://github.com/bbcrd/bbc-vamp-plugins.Last visited 02/29/2014.

115

[12] Steven Davis and Paul Mermelstein. Comparison of parametric representationsfor monosyllabic word recognition in continuously spoken sentences. Acoustics,Speech and Signal Processing, IEEE Transactions on, 28(4):357–366, 1980.

[13] Jeffrey Dean and Sanjay Ghemawat. Mapreduce: simplified data processing onlarge clusters. Communications of the ACM, 51(1):107–113, 2008.

[14] Giuseppe DeCandia, Deniz Hastorun, Madan Jampani, Gunavardhan Kakula-pati, Avinash Lakshman, Alex Pilchin, Swaminathan Sivasubramanian, PeterVosshall, and Werner Vogels. Dynamo: amazon’s highly available key-valuestore. In SOSP, volume 7, pages 205–220, 2007.

[15] Francois Deliege, Bee Yong Chua, and Torben Bach Pedersen. High-level audiofeatures: Distributed extraction and similarity search. In ISMIR, pages 565–570,2008.

[16] Simon Dixon. Onset detection revisited. In Proceedings of the 9th InternationalConference on Digital Audio Effects, volume 120, pages 133–137, 2006.

[17] Florian Eyben, Martin Wollmer, and Bjorn Schuller. Opensmile: the munichversatile and fast open-source audio feature extractor. In Proceedings of theinternational conference on Multimedia, pages 1459–1462. ACM, 2010.

[18] Roy T Fielding and Richard N Taylor. Principled design of the modern webarchitecture. ACM Transactions on Internet Technology (TOIT), 2(2):115–150,2002.

[19] Vamp Plugin Framework. http://www.vamp-plugins.org/. Last visited03/21/2014.

[20] Matteo Frigo and Steven G. Johnson. The design and implementation of FFTW3.Proceedings of the IEEE, 93(2):216–231, 2005. Special issue on “Program Gen-eration, Optimization, and Platform Adaptation”.

[21] Olivier Gillet and Gael Richard. Automatic transcription of drum loops. InAcoustics, Speech, and Signal Processing, 2004. Proceedings.(ICASSP’04). IEEEInternational Conference on, volume 4, pages iv–269. IEEE, 2004.

[22] Neil J. Gunther. Guerrilla Capacity Planning. Springer Berlin Heidelberg, 2007.

[23] Naohiro Hayashibara, Xavier Defago, Rami Yared, and Takuya Katayama. Theϕ accrual failure detector. In Reliable Distributed Systems, 2004. Proceedings ofthe 23rd IEEE International Symposium on, pages 66–78. IEEE, 2004.

[24] Gregor Hohpe and Bobby Woolf. Enterprise integration patterns: Designing,building, and deploying messaging solutions. Addison-Wesley Professional, 2004.

[25] Scala Programming Language. http://www.scala-lang.org/. Last visited11/18/2013.

116

[26] Olivier Lartillot, Petri Toiviainen, and Tuomas Eerola. A matlab toolbox formusic information retrieval. In Data analysis, machine learning and applications,pages 261–268. Springer, 2008.

[27] Benoit Mathieu, Slim Essid, Thomas Fillon, Jacques Prado, and Gael Richard.Yaafe, an easy to use and efficient audio feature extraction software. In ISMIR,pages 441–446, 2010.

[28] Daniel McEnnis, Cory McKay, and Ichiro Fujinaga. Overview of omen. In ISMIR,pages 7–12, 2006.

[29] Daniel McEnnis, Cory McKay, Ichiro Fujinaga, and P Depalle. jaudio: Additionsand improvements. In ISMIR, pages 385–386, 2006.

[30] Cory McKay, Ichiro Fujinaga, and Philippe Depalle. jaudio: A feature extractionlibrary. In Proceedings of the International Conference on Music InformationRetrieval, pages 600–3, 2005.

[31] University of Colorado Colorado Springs. EAS Cloud. http://www.uccs.edu/

eas/eas_cloud.html. Last visited 03/15/2014.

[32] QM Vamp Plugins. http://isophonics.net/QMVampPlugins. Last visited02/29/2014.

[33] Apple Press. http://www.apple.com/pr/library/2013/02/

06iTunes-Store-Sets-New-Record-with-25-Billion-Songs-Sold.html.Last visited 03/29/2014.

[34] Apache Software Foundation. HDFS Short-Circuit Local Reads. http:

//hadoop.apache.org/docs/r2.2.0/hadoop-project-dist/hadoop-hdfs/

ShortCircuitLocalReads.html. Last visited 03/05/2014.

[35] Devon Bryant. Scalable Audio Feature Extraction. Source Repository. http:

//github.com/devonbryant/safe. Last visited 04/10/2014.

[36] Devon Bryant. Numerical Computing With Scala. http://devonbryant.

github.io/blog/2013/03/03/numerical-computing-with-scala/. Last vis-ited 03/25/2014.

[37] Devon Bryant. MIR Tool Evaluation Scripts. http://github.com/

devonbryant/mir-eval-scripts. Last visited 04/10/2014.

[38] Konstantin Shvachko, Hairong Kuang, Sanjay Radia, and Robert Chansler.The hadoop distributed file system. In Mass Storage Systems and Technologies(MSST), 2010 IEEE 26th Symposium on, pages 1–10. IEEE, 2010.

[39] Spotify. http://press.spotify.com/us/information/. Last visited03/29/2014.

117

[40] Herb Sutter and James Larus. Software and the concurrency revolution. Queue,3(7):54–62, September 2005.

[41] George Tzanetakis and Perry Cook. Marsyas: A framework for audio analysis.Organised sound, 4(3):169–175, 2000.

[42] Robbert Van Renesse, Yaron Minsky, and Mark Hayden. A gossip-style failuredetection service. In Middleware’98, pages 55–70. Springer, 1998.

[43] Eclipse Public License version 1.0. http://www.eclipse.org/org/documents/epl-v10.html. Last visited 04/10/2014.

118