1. Musical Genre Categorization Using Support Vector Machines Shu Wang

2. Outline Motivation Dataset Feature Extraction Automatic Classification Conclusion

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

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 Cepstral Coefficients …. 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 36042311123 04200020001 433650059613 401342021415 10023602183 14200463024 00210036113 00135011773 20004003220 21430104115

12. K-Nearest Neighbors Average Correct Classification Rate 0.6856

13. Support Vector Machine Correct Classification Rate 0.6900 Confusion Matrix 35311022159 03601010001 32323022054 10436402582 10003900120 07000411010 20101136001 00255004038 11311002261 71730371024

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