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Musical Genre Categorization Using Support Vector Machines

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Musical Genre Categorization Using Support Vector Machines. Shu Wang. Outline. Motivation Dataset Feature Extraction Automatic Classification Conclusion. Motivation. Music Information Retrieval. Music Genres. http://www.flickr.com/photos/elbewerk/2845839180/lightbox/. Dataset.

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Musical Genre Categorization Using Support Vector Machines

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  1. Musical Genre Categorization Using Support Vector Machines Shu Wang

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

  3. Motivation • Music Information Retrieval Music Genres http://www.flickr.com/photos/elbewerk/2845839180/lightbox/

  4. Dataset • GTZAN Genre Collection • 10 Genres • 30 Seconds Audio Waveform • 1000 Tracks Dataset: http://marsyas.info/download/data_sets/

  5. Feature Extraction • Features Selection (38 Features) • Time Domain Zero Crossings • Mel-Frequency CepstralCoefficients • …. • Tool • MIRtoolbox https://www.jyu.fi/hum/laitokset/musiikki/en/research/coe/materials/mirtoolbox

  6. Automatic Classification • Approach • K-Nearest Neighbors • Support Vector Machine • KNN-SVM Method

  7. 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

  8. Training Process • Each Classifier has high Classification Rate.

  9. Training Process

  10. Testing Process • Combination Rules • Voting

  11. K-Nearest Neighbors • Correct Classification Rate • 0.6400 • Confusion Matrix 36 0 4 2 3 1 1 1 2 3 0 42 0 0 0 2 0 0 0 1 4 3 36 5 0 0 5 9 6 13 4 0 1 34 2 0 2 14 1 5 1 0 0 2 36 0 2 1 8 3 1 4 2 0 0 46 3 0 2 4 0 0 2 1 0 0 36 1 1 3 0 0 1 3 5 0 1 17 7 3 2 0 0 0 4 0 0 3 22 0 2 1 4 3 0 1 0 4 1 15

  12. K-Nearest Neighbors • Average Correct Classification Rate • 0.6856

  13. Support Vector Machine • Correct Classification Rate • 0.6900 • Confusion Matrix 35 3 1 1 0 2 2 1 5 9 0 36 0 1 0 1 0 0 0 1 3 2 32 3 0 2 2 0 5 4 1 0 4 36 4 0 2 5 8 2 1 0 0 0 39 0 0 1 2 0 0 7 0 0 0 41 1 0 1 0 2 0 1 0 1 1 36 0 0 1 0 0 2 5 5 0 0 40 3 8 1 1 3 1 1 0 0 2 26 1 7 1 7 3 0 3 7 1 0 24

  14. Support Vector Machine • Average Correct Classification Rate • 0.6526

  15. 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

  16. KNN & SVM • Average Correct Classification Rate • 0.6928

  17. 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

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