musical genre categorization using support vector machines shu wang
DESCRIPTION
Motivation Music Information Retrieval Music GenresTRANSCRIPT
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