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http://dx.doi.org/10.7776/ASK.2007.26.8.390

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

Song, Ji-Hyun (인하대학교 전자전기공학부)
Lee, Kye-Hwan (인하대학교 전자전기공학부)
Chang, Joon-Hyuk (인하대학교 전자전기공학부)
Publication Information
The Journal of the Acoustical Society of Korea / v.26, no.8, 2007 , pp. 390-396 More about this Journal
Abstract
In this letter, we propose a novel approach to improve the performance of speech/music classification for the selectable mode vocoder(SMV) of 3GPP2 using the Gaussian mixture model(GMM) which is based on the expectation-maximization(EM) algorithm. We first present an effective analysis of the features and the classification method adopted in the conventional SMV. And then feature vectors which are applied to the GMM 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
Speech/Music classification algorithm; Selectable mode vocoder(SMV); Gaussian mixture model(GMM);
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