Multi-objective feature selection in music genre and style recognition tasks
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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In this paper we propose a new set of parameters for audio signal analysis and classification. These parameters are regressions computed on the normalized modulation spectrum of high-resolution cepstral coefficients. The parameter set is scalable in its size and gives a compact representation of the modulation content of speech and other audio signals. These parameters as well as the regression approximation error are well suited for characterizing audio signals in a unified framework. In particular we use a set of eight parameters in a speech/music/noise classification task in which we achieve a classification accuracy which compares very well with other approaches including static and dynamic MFCCs.