Musical genre classification using modified wavelet-like features and support vector machines

Authors:
Oleg Kotov;Aliaksandr Paradzinets;Eugeny Bovbel
Affiliations:
Belarusian State University, Kurchatova, Belarus;Ecole Centrale de Lyon, Kurchatova, Belarus;Belarusian State University, Kurchatova, Belarus
Venue:
EurolMSA '07 Proceedings of the Third IASTED European Conference on Internet and Multimedia Systems and Applications
Year:
2007

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Abstract

There are two major stages in musical genre classification: feature extraction and classification. While the second stage implies a choice of a variety of machine leaning methods (SVMs, neural networks, etc), the first stage plays crucial part in perfomance and accuracy of the classification system, providing much more creativity in development of different feature extraction methods. In this paper we present initial study of feature extraction based on wavelets and pseudo-wavelets in the area of musical genre classification. A new type of feature vector, based on continuous wavelet and wavelet-like transform of input audio data is proposed. Support vector machine was used as a classifier for testing the feature extraction procedure. The results of our experimental study are shown.