Classification of Dance Music by Periodicity Patternsc.chuan/cis4930/Aspillaga-w8.pdf ·...

Classification of Dance Music by Periodicity Patterns- Simon Dixon, Elias Pampalk, and Gerhard Widnmer

Felipe X. AspillagaCIS 5930 - Spring 2011

Music Informatics and ComputingDr. Ching-Hua Chuan

1

Monday, February 28, 2011

Outline

Problem Overview

Inter-Onset Interval Clustering

Periodicity Detection with Autocorrelation

Determining Tempo, Meter and Style

Tests, Comparisons and Results

Conclusion

2


Distinguish and determine subtleties of similar genres

Classification of widely differing genre classification relatively simple

Use a focused set of latin and ballroom dance genres

E.g. Tango, Foxtrot, Samba, Cha Cha, Rumba, etc

Use only temporal features (Related to time)

Other features such as instrumentation and articulation not used

Problem Overview

3


Compare two methods of determining periodicities

Onset Detection (Dixon et. al.)

Detect note onsets in audio signal

Calculate time intervals between onset pairs

Find significant periodicities using clustering

Similarity of rhythmic patterns (Paulus & Klapuri)

Split audio into frequency bands

Produce set of amplitude envelopes

Use autocorrelation to find periodicities in each band

Problem Overview

4


“Post periodicities”

Assign each periodicity to a note value

Find best matching metrical hierarchy

Tempo and meter

Determine style/genre

Problem Overview

5



Step 1: Audio Processing

Read audio file (Linear PCM)

Converted to single channel

Process in 40ms blocks

Calculates RMS amplitude

Find local peaks slopes using time-domain method(Figure 1)

Eliminate insignificant peaks by setting thresholds for amplitude and slope

6



Step 2: Clustering

Calculate IOI pairs < 5 secs apart

Weight based on onset amplitude

Sum equally spaced pairs

Create weighted IOI histogram (Fig. 3)

Best-first cluster has largest average amplitude

Next-best clusters determined from “left-over” IOI’s

Cluster width adjusted by IOI duration

Associate related clusters (1/2, 1/4)

Adjust weight based on related clusters

7

1 4 8

Audio: http://en.wikipedia.org/wiki/Samba


http://en.wikipedia.org/wiki/Samba

http://en.wikipedia.org/wiki/Samba

8


Onset times <= 5 secs //Group equal onsets //

Determine each onset weight //Look for un-clustered IOI’s //

Onset times <= 5 secs //Allow time variation in interval //

Changes per ‘while’ iteration //Find max of unmarked clusters //

Create current max cluster //Ignore this set in future ‘while’s //

All clusters now ordered //Double / half tempos //

Weight based on relatives //

Clustering Algorithm



Step 1: Audio Processing

Read audio file (Linear PCM)

Converted to single channel

Pass through 8 filter bands

One for each octave

Reduce sampling rate: 980 Hz

9



Step 2: Periodicity Calculation

Find periodicities for each band

Top 3 Peaks found in autocorrelation with lags <= 5 sec.

Discard the lag 0 value

Match peaks across bands

Weights determined by sum of mean autocorrelation value and number of matched periodicities in the set

Ranking helps to choose tempo, meter and style

10


Time for a breather!Style is everything

Jim Carey’s Cha - Cha

http://www.youtube.com/

Choose your own adventure

Carlos Gardel - ‘Por Una Cabeza’

Al Pacino and Gabrielle Anwar's Tango

Tina and Felipe’s Tango

11


http://www.youtube.com/watch_popup?v=ityEgXvVBf4&vq=small

http://www.youtube.com/watch_popup?v=ityEgXvVBf4&vq=small

http://www.youtube.com/watch_popup?v=VtMp5wy21Dc&vq=hd720

http://www.youtube.com/watch_popup?v=VtMp5wy21Dc&vq=hd720


Both approaches discover peaks that correlate to beat and measure well

Difficulty lies with discerning peaks occurring at various metrical levels (Doubling / Halves / etc)

Step 1: Find the Measure level

Compare each periodicity against all others, with integer simplification to solve for Error Weight

Step 2: Calculate the weighted sums of the periodicity weights

Separate odd and even meters to account in variance in distribution (3/4 vs. 4/4 time)

W-sums (even || odd) = Sum of periods(W-meter * W-periodicity * W-tempo * W-error)

Step 3: Choose the style

Use a rule-based approach

Filter by Tempo an Meter definitions

Then use periodicity distribution to distinguish by style

12



Example: Cha Cha vs. Tango vs. Slow Fox

Tempos for each lie below the 35 mpm range

Meter for each is 4

Lastly, look at weighted periodicities

Max weight = 1/2 note: Slow Fox

Sum the weight of the 1/8 notes

High Value = Cha Cha

Low Value = Tango

13



161 test tracks come from CD audio with MPM and Style provided

‘Pseudo’-ground truth

Periodicity position of Measure and Beat in ranking lists

IOI finds more significant periodicities

Correlation holds higher ranking of significant periodicities found

14



Autocorrelation seem to produce more accurate end results for determining Tempo, Meter and Style.

Although Tempo results were decent, Identification of meter and Style had a good success rate, especially with Correlation

Tempo errors in both favor halving/doubling

Correlation had very few outside these bounds

Confusion matrix shows tendency to mix up similar genres

E.g. Cha Cha - Tango - Slow Fox

15


ConclusionsSuccesses

Tempo determination for Correlation whiffed only 5 times

Meter results in both very strong

Classification works well with correlation when correct measure level is chosen

Difficulties

No ground truth available

Categories subjective / cross-over

Small test set

Tightly coupled definitions

Prone to half/doubling errors

Future Improvements

Learning machine automates genre pattern definitions

Approach may generalize towards a wider variety of music

Random Thoughts

Tempo results given a 3 MPM grace window...seems generous

Can this be applied in real-time and/or without the need to decode compressed audio?

More refinement need regarding halving / doubling

16


17


Time for a breather!

18


Classification of Dance Music by Periodicity Patternsc.chuan/cis4930/Aspillaga-w8.pdf ·...

Documents

Transcript of Classification of Dance Music by Periodicity Patternsc.chuan/cis4930/Aspillaga-w8.pdf ·...