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Adaptive Kernel Function of SVM for Improving Speech/Music Classification of 3GPP2 SMV

Title
Adaptive Kernel Function of SVM for Improving Speech/Music Classification of 3GPP2 SMV
Other Titles
Music Classification of 3GPP2 SMV
Author
장준혁
Keywords
SVM; ,SMV; ,speech/music classification algorithm
Issue Date
2011-12
Publisher
Electronics and Telecommunications Research Institute(한국전자통신연구원)
Citation
ETRI Journal, 2011, 33(6), P.871-879, P.9
Abstract
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.
URI
https://onlinelibrary.wiley.com/doi/abs/10.4218/etrij.11.0110.0780http://hdl.handle.net/20.500.11754/66773
ISSN
1225-6463
DOI
10.4218/etrij.11.0110.0780
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > ELECTRONIC ENGINEERING(융합전자공학부) > Articles
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