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Musical Genre Categorization Using Support Vector Machines

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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