Title: Type-2 Fuzzy Gaussian mixture models for singing voice classification in commercial music production

Authors: Faiz Maazouzi; Halima Bahi

Addresses: LabGed Laboratory, BP. 12 The University of Annaba, Algeria ' LabGed Laboratory, BP. 12 The University of Annaba, Algeria

Abstract: The paper describes a system of singing voice classification in the commercial music productions. A first step in our system is to separate the singer's voice from the music. Based on the vocal part, two sets of parameters are formed, one for singing voice type and the other for the singing voice quality. Each set of parameters contains a number of MPEG-7 low-level descriptors and other descriptors; at the classification stage the paper suggests an extension of Gaussian Mixture Models (GMMs), by using the Type-2 FGMMs (Type-2 Fuzzy Gaussian Mixture Models). Results show substantial improvements when compared to similar works.

Keywords: music information retrieval; feature extraction; feature selection; singing voice classification; fuzzy Gaussian mixture models; voice-music separation; commercial music production; voice type; voice quality; fuzzy modelling.

DOI: 10.1504/IJSISE.2013.053418

International Journal of Signal and Imaging Systems Engineering, 2013 Vol.6 No.2, pp.111 - 118

Received: 12 Sep 2011
Accepted: 12 Jan 2012
Published online: 21 Apr 2013 *

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