Analysis and Improvement of Speech/Music Classification for 3GPP2 SMV Based on GMM

Abstract

In this letter, a novel approach is proposed to improve the performance of speech/music classification for the selectable mode vocoder (SMV) of 3GPP2 using the Gaussian mixture model (GMM). An in-depth analysis of the features and classification method adopted in the conventional SMV is performed. Feature vectors applied to the GMM are then selected from the relevant parameters of the SMV for efficient speech/music classification. The performance of the proposed algorithm is evaluated under various conditions and yields better results compared with the conventional scheme implemented in the SMV.