文章基本信息

标题：Adaptive Kernel Function of SVM for Improving Speech/Music Classification of 3GPP2 SMV
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作者：Lim, Chung-Soo ; Chang, Joon-Hyuk
期刊名称：ETRI Journal
印刷版ISSN：1225-6463
电子版ISSN：2233-7326
出版年度：2011
卷号：33
期号：6
页码：871-879
DOI：10.4218/etrij.11.0110.0780
语种：English
出版社：Electronics and Telecommunications Research Institute
摘要：Because a wide variety of multimedia services are provided through personal wireless communication devices, the demand for efficient bandwidth utilization becomes stronger. This demand naturally results in the introduction of the variable bitrate speech coding concept. One exemplary work is the selectable mode vocoder (SMV) that supports speech/music classification. However, because it has severe limitations in its classification performance, a couple of works to improve speech/music classification by introducing support vector machines (SVMs) have been proposed. While these approaches significantly improved classification accuracy, they did not consider correlations commonly found in speech and music frames. In this paper, we propose a novel and orthogonal approach to improve the speech/music classification of SMV codec by adaptively tuning SVMs based on interframe correlations. According to the experimental results, the proposed algorithm yields improved results in classifying speech and music within the SMV framework.
关键词：SVM;SMV;speech/music classification algorithm