musical genre categorization using support vector machines shu wang

Musical Genre Categorization Using Support Vector Machines

Shu Wang

Outline• Motivation• Dataset• Feature Extraction• Automatic Classification • Conclusion

Motivation• Music Information Retrieval

http://www.flickr.com/photos/elbewerk/2845839180/lightbox/ Music Genres

Dataset • GTZAN Genre Collection

• 10 Genres• 30 Seconds Audio Waveform• 1000 Tracks

Dataset: http://marsyas.info/download/data_sets/

Feature Extraction• Features Selection (38 Features)

• Time Domain Zero Crossings• Mel-Frequency Cepstral Coefficients• ….

• Tool• MIRtoolbox

https://www.jyu.fi/hum/laitokset/musiikki/en/research/coe/materials/mirtoolbox 

Automatic Classification • Approach

• K-Nearest Neighbors• Support Vector Machine• KNN-SVM Method

Automatic Classification • Difficulty

• Multiclass Classification Problem• Approach

• One versus Rest• Con: Unbalanced Training Data and Lower Sensitivity

and Specificity• One versus One & Classifier of Classifiers

Training Process

• Each Classifier has high Classification Rate.

Classifier #1 Classifier #2 Classifier #45… …

Blue&Classical Blue&Country Reggae&Rock

Training Process

Testing Process• Combination Rules

• Voting

Classifier #1 Classifier #2 Classifier #45… …

Combination Rules

Testing Features

Final Output

K-Nearest Neighbors• Correct Classification Rate

• 0.6400• Confusion Matrix

36 0 4 2 31 1 1 23

0 42 0 0 02 0 0 01

4 3 36 5 00 5 9 613

4 0 1 34 20 2 14 15

1 0 0 2 360 2 1 83

1 4 2 0 046 3 0 24

0 0 2 1 00 36 1 13

0 0 1 3 50 1 17 73

2 0 0 0 40 0 3 220

2 1 4 3 01 0 4 115

K-Nearest Neighbors• Average Correct Classification Rate

• 0.6856

Support Vector Machine• Correct Classification Rate

• 0.6900• Confusion Matrix

35 3 1 1 02 2 1 59

0 36 0 1 01 0 0 01

3 2 32 3 02 2 0 54

1 0 4 36 40 2 5 82

1 0 0 0 390 0 1 20

0 7 0 0 041 1 0 10

2 0 1 0 11 36 0 01

0 0 2 5 50 0 40 38

1 1 3 1 10 0 2 261

7 1 7 3 03 7 1 024

Support Vector Machine• Average Correct Classification Rate

• 0.6526

KNN & SVM• Correct Classification Rate

• 0.7100• Confusion Matrix

40 0 2 2 4 3 1 0 6 1 0 45 0 0 0 3 0 0 0 1 4 1 39 4 0 0 1 4 1 8 1 0 0 30 1 0 3 5 2 2 0 0 0 0 37 0 0 2 13 2 0 2 1 0 0 42 2 0 1 0 2 0 2 1 1 1 41 0 0 7 1 1 1 5 6 0 0 34 4 0 1 0 1 3 1 0 0 1 20 2 1 1 4 5 0 1 2 4 3 27

KNN & SVM• Average Correct Classification Rate

• 0.6928

Conclusion• We achieve over 65% Correct Classification

Rate in this Multiclass Classification Problem

• KNN and SVM method based on One versus One is a promising way to solve the Automatic Genres Classification Problem