Authors: MEHMET ERDAL ÖZBEK, MEHMET EMRE ÇEK, FERİT ACAR SAVACI
Abstract: Music information retrieval and particularly musical instrument classification has become a very popular research area for the last few decades. Although in the literature many feature sets have been proposed to represent the musical instrument sounds, there is still need to find a superior feature set to achieve better classification performance. In this paper, we propose to use the parameters of skewed alpha-stable distribution of sub-band wavelet coefficients of musical sounds as features and show the effectiveness of this new feature set for musical instrument classification. We compare the classification performance with the features constructed from the parameters of generalized Gaussian density and some of the state-of-the-art features using support vector machine classifiers.
Keywords: Musical instrument classification, skewed alpha-stable distribution, generalized Gaussian density, support vector machine
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