Skewed alpha-stable distributions for modeling and classification of musical instruments

Authors

MEHMET ERDAL ÖZBEK
MEHMET EMRE ÇEK
FERİT ACAR SAVACI

DOI

10.3906/elk-1102-1031

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

First Page

934

Last Page

947

Recommended Citation

ÖZBEK, MEHMET ERDAL; ÇEK, MEHMET EMRE; and SAVACI, FERİT ACAR (2012) "Skewed alpha-stable distributions for modeling and classification of musical instruments," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 20: No. 6, Article 8. https://doi.org/10.3906/elk-1102-1031
Available at: https://journals.tubitak.gov.tr/elektrik/vol20/iss6/8