Use of Gaussian Mixture Models and Vector quantization for singing voice classification in commercial music productions

Abstract

Instead of the expansion of the information retrieval systems, the music information retrieval domain is still an open one. In this context, the singing voice classification is a promised trend. In this paper, we shall present our experiments concerning the classification of singers according to their voice type, and their voice quality. Some experiments were carried in which two sets of parameters are used in addition to the use of two classification approaches: The GMM (Gaussian Mixture Models) and the VQ (Vector quantization). The obtained results were compared to those provided by the related state-of-the-art approaches.