A N APPROACH TO AUTOMATIC MUSIC PLAYLIST GENERATION USING I T UNES AND BEHAVIORAL DATA By Darrius...
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Transcript of A N APPROACH TO AUTOMATIC MUSIC PLAYLIST GENERATION USING I T UNES AND BEHAVIORAL DATA By Darrius...
AN APPROACH TO AUTOMATIC MUSIC PLAYLIST GENERATION USING ITUNES AND BEHAVIORAL DATABy Darrius Serrant, Undergraduate
Supervised by Mitsunori Ogihara, PhD
CSC410: Computer Science Project Planning
AT A GLANCE
Motivation Automatic Playlist Generation Problem Related Work Scope of Project System Features Process Overview Testing and Evaluation
MOTIVATION
Music: food for the soul! Smorgasbord of expressions, emotions, and
representations Binds us to friends, memories, experiences, etc… Marketable, available and consumable
The typical music library 1,000+ titles Diverse in features Difficult to organize, explore, and experience
AUTOMATIC PLAYLIST GENERATION PROBLEM
Manual playlist creation Burdensome and time consuming Subjective
Automatic playlist creation: Create music playlists fulfilling arbitrary
requirements # of titles Permutation Measure of variety
An NP-hard problem
RELATED WORK
Scalable search algorithms1
Search algorithms based on skipping behavior2
Reduction to the traveling salesman problem3
Local search CSP algorithm4
Case-base approach to playlist generation5
Song selection via a network flow model6
The Music Genome Project7
RELATED WORK (CONTINUED)
Commonalities: Assumes limited knowledge of music library Assumes usage of audio feature extraction
techniques Requires explicit specification of playlist
constraints
SCOPE OF PROJECT
A unique approach to the automatic playlist generation problem Eliminates explicit user specifications Adapts to users’ listening preferences More expressive than audio features extraction
Research objectives Analyze contents of users’ music library Monitor and learn users’ listening habits Generate playlists of twelve songs by request
SYSTEM FEATURES
iTunes Library Data Extraction Extract music titles and their characteristics
Song Characteristics Aggregator Collect metadata from Internet sources
Machine Learning Statistically model users’ music listening habits
Playlist Generation Build a playlist from a “playlist” state space
SYSTEM FEATURES (CONTINUED)
User Feedback Evaluation of generated playlists Periodical mood assessments Software application monitoring
PROCESS OVERVIEW
1. User listens to music through iTunes1. Monitor systems’ active processes2. Monitor local weather forecasts3. Receive user’s mood updates
2. User closes down iTunes3. Begin pre-playlist generation tasks
1. Collect data from user’s iTunes Music Library2. Collect data from Internet sources3. Update user’s listening pattern
PROCESS OVERVIEW (CONTINUED)
4. Automatically generate a new playlist1. Extract search heuristics from listening pattern.2. Build a new playlist from the search space.
5. User evaluates the generated playlist6. Incorporate user feedback into listening
pattern
TESTING AND EVALUATION
Phase One: Theoretical Testing Under simulated conditions Tasks:
Evaluate scalability of search algorithms Verify production of desired playlists for “naïve” users
Phase Two: Live Testing Deliver product to actual users Tasks:
Evaluate scalability of search algorithms for Mac and PC users
Verify production of desired playlists for “actual” users Test effects of volatile mood and environmental
changes on playlist generation.
CURRENT AND FUTURE WORK
Version 1.0 in development iTunes Data Extractor
Apache Xerces 2.7 XML Parser Data Collectors
Mood Collection System Process Collection Listening Pattern Assembly
Machine Learning Weka 3.6 Supervised Learning Algorithms Decision Tree Learning
Search Algorithms Breadth-first search Local beam search Genetic algorithm