Speech/Music Classification Enhancement for 3GPP2 SMV Codec Based on Support Vector Machine

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概要

• 論文の詳細を見る

• In this letter, we propose a novel approach to speech/music classification based on the support vector machine (SVM) to improve the performance of the 3GPP2 selectable mode vocoder (SMV) codec. We first analyze the features and the classification method used in real time speech/music classification algorithm in SMV, and then apply the SVM for enhanced speech/music classification. For evaluation of performance, we compare the proposed algorithm and the traditional algorithm of the SMV. The performance of the proposed system is evaluated under the various environments and shows better performance compared to the original method in the SMV.

• （社）電子情報通信学会の論文
• 2009-02-01

著者

• KIM Sang-Kyun

  Department of Electronic Engineering, Inha University

• CHANG Joon-Hyuk

  Department of Electronic Engineering, Inha University

• Kim Sang-kyun

  Department Of Ceramic Engineering Hanyang University

• Chang Joon-hyuk

  Department Of Electrical And Computer Engineering University Of California At Santa Barbara

• Kim Sang-kyun

  Department Of Electronic Engineering Inha University

• Kim Sang‐kyun

  Inha Univ. Incheon Kor

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