Classification of performers using support vector machines

Abstract

Huge amount of different music material in digital form, that can be found on the Internet, represents a big problem for a user who wants to find some particular music piece. Indexing, retrieving and classification are some of the techniques that can be used to provide faster search. In this paper, one of the methods for classification of the performers is described. We created audio databases, which consist of short audio sequences from several (nine) songs, sang by three distinct performers. Wavelet coefficients were used as feature descriptors for these music sequences. Classification was performed based on linear support vector machines. Our system exhibits very good results in the experiment with two performers (two classes): partial accuracies for classes were 100% and 98%, respectively. In the experiment with three classes (three performers), we used one-versus-one approach, and obtained partial accuracies of 50%, 82% and 35%, respectively.

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