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Analysis and Implementation of Speech/Music Classification for 3GPP2 SMV Codec Based on Support Vector Machine  

Kim, Sang-Kyun (Department of Electronics Engineering, Inha University)
Chang, Joon-Hyuk (Department of Electronics Engineering, Inha University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.45, no.6, 2008 , pp. 142-147 More about this Journal
Abstract
In this paper, we propose a novel a roach to improve the performance of speech/music classification for the selectable mode vocoder (SMV) of 3GPP2 using the support vector machine (SVM). The SVM makes it possible to build on an optimal hyperplane that is separated without the error where the distance between the closest vectors and the hyperplane is maximal. We first present an effective analysis of the features and the classification method adopted in the conventional SMV. And then feature vectors which are a lied to the SVM are selected from relevant parameters of the SMV for the efficient speech/music classification. The performance of the proposed algorithm is evaluated under various conditions and yields better results compared with the conventional scheme of the SMV.
Keywords
Support Vector Machine(SVM); Selectable Mode Vocoder(SMV); Speech/Music Classification Algorithm;
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