The Journal of the Acoustical Society of Korea (한국음향학회지)

The Acoustical Society of Korea (한국음향학회)
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Analysis and Implementation of Speech/Music Classification for 3GPP2 SMV Codec Employing SVM Based on Discriminative Weight Training

SMV코덱의 음성/음악 분류 성능 향상을 위한 최적화된 가중치를 적용한 입력벡터 기반의 SVM 구현

  • 김상균 (인하대학교 전자공학부) ;
  • 장준혁 (인하대학교 전자공학부) ;
  • 조기호 (서울대학교 전기컴퓨터공학부) ;
  • 김남수 (서울대학교 전기컴퓨터공학부)
  • Published : 2009.07.31
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Abstract

In this paper, we apply a discriminative weight training to a support vector machine (SVM) based speech/music classification for the selectable mode vocoder (SMV) of 3GPP2. In our approach, the speech/music decision rule is expressed as the SVM discriminant function by incorporating optimally weighted features of the SMV based on a minimum classification error (MCE) method which is different from the previous work in that different weights are assigned to each the feature of SMV. The performance of the proposed approach is evaluated under various conditions and yields better results compared with the conventional scheme in the SVM.

본 논문에서는 변별적 가중치 학습 (discriminative weight training) 기반의 최적화된 가중치를 가지는 입력벡터를 구성하여 support vector machine (SVM)을 이용한 기존의 3GPP2 selectable mode vocoder (SMV)코덱의 음성/음악 분류 성능을 향상 시키는 방법을 제안한다. 구체적으로, 최소 분류 오차 minimum classification error (MCE) 방법을 도입하여, 최적화된 가중치를 각각의 특징벡터별로 부가한 SVM을 적용하여 기존의 가중치를 고려하지 않은 SVM 기반의 알고리즘과 비교하였으며, 우수한 음성/음악 분류 성능을 보였다.

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References

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