Journal of the Institute of Electronics and Information Engineers (전자공학회논문지)

The Institute of Electronics and Information Engineers (대한전자공학회)
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Speech/Mixed Content Signal Classification Based on GMM Using MFCC

MFCC를 이용한 GMM 기반의 음성/혼합 신호 분류

  • Kim, Ji-Eun (Department of Radio Engineering, ChungBuk University) ;
  • Lee, In-Sung (Department of Radio Engineering, ChungBuk University)
  • 김지은 (충북대학교 전파통신 공학과) ;
  • 이인성 (충북대학교 전파통신 공학과)
  • Received : 2012.08.24
  • Published : 2013.02.25
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Abstract

In this paper, proposed to improve the performance of speech and mixed content signal classification using MFCC based on GMM probability model used for the MPEG USAC(Unified Speech and Audio Coding) standard. For effective pattern recognition, the Gaussian mixture model (GMM) probability model is used. For the optimal GMM parameter extraction, we use the expectation maximization (EM) algorithm. The proposed classification algorithm is divided into two significant parts. The first one extracts the optimal parameters for the GMM. The second distinguishes between speech and mixed content signals using MFCC feature parameters. The performance of the proposed classification algorithm shows better results compared to the conventionally implemented USAC scheme.

본 논문에서는 MFCC를 이용한 GMM 기반의 음성과 혼합 신호 분류 알고리즘을 MPEG의 표준 코덱인 USAC에 적용하였다. 효과적인 패턴 인식을 위해 GMM을 이용하였고, EM알고리즘을 사용하여 최적의 GMM 파라미터를 추출하였다. 제안하는 분류 알고리즘은 두 가지 중요한 부분으로 나뉜다. 첫째는 GMM을 통해 최적의 파라미터를 추출하는 것 이고, 두 번째는 MFCC 값을 이용한 패턴인식을 통해 음성/혼합 신호를 분류하였다. 제안된 알고리즘의 성능을 평가한 결과 MFCC를 이용한 GMM 기반의 제안된 방법이 기존 USAC의 방법보다 우수한 음성/혼합 신호 분류 성능을 보였다.

Keywords

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