Musical Genre Categorization Using Support Vector Machines Shu Wang

Outline Motivation Dataset Feature Extraction Automatic Classification Conclusion

Motivation Music Information Retrieval Music Genres

Dataset GTZAN Genre Collection 10 Genres 30 Seconds Audio Waveform 1000 Tracks Dataset:

Feature Extraction Features Selection (38 Features) Time Domain Zero Crossings Mel-Frequency Cepstral Coefficients …. Tool 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.

Training Process

Testing Process Combination Rules Voting

K-Nearest Neighbors Correct Classification Rate Confusion Matrix

K-Nearest Neighbors Average Correct Classification Rate

Support Vector Machine Correct Classification Rate Confusion Matrix

Support Vector Machine Average Correct Classification Rate

KNN & SVM Correct Classification Rate Confusion Matrix

KNN & SVM Average Correct Classification Rate

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