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Discriminative Weight Training for Support Vector Machine-Based Speech/Music Classification in 3GPP2 SMV Codec

Sang-Kyun KIM, Joon-Hyuk CHANG

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Summary :

In this study, a discriminative weight training is applied to a support vector machine (SVM) based speech/music classification for a 3GPP2 selectable mode vocoder (SMV). In the proposed approach, the speech/music decision rule is derived by the SVM by incorporating optimally weighted features derived from the SMV based on a minimum classification error (MCE) method. This method differs from that of the previous work in that different weights are assigned to each feature of the SMV a novel process. According to the experimental results, the proposed approach is effective for speech/music classification using the SVM.

Publication
IEICE TRANSACTIONS on Fundamentals Vol.E93-A No.1 pp.316-319
Publication Date
2010/01/01
Publicized
Online ISSN
1745-1337
DOI
10.1587/transfun.E93.A.316
Type of Manuscript
LETTER
Category
Speech and Hearing

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