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Gaussian Mixture Model using Minimum Classification Error for Environmental Sounds Recognition Performance Improvement

Minimum Classification Error 방법 도입을 통한 Gaussian Mixture Model 환경음 인식성능 향상

  • 한다정 (전남대학교 전자컴퓨터공학부) ;
  • 박아론 (전남대학교 전자컴퓨터공학부) ;
  • 박준규 (전남대학교 전자컴퓨터공학부) ;
  • 백성준 (전남대학교 전자컴퓨터공학부)
  • Received : 2011.09.22
  • Accepted : 2011.12.09
  • Published : 2011.12.28

Abstract

In this paper, we proposed the MCE as a GMM training method to improve the performance of environmental sounds recognition. We model the environmental sounds data with newly defined misclassification function using the log likelihood of the corresponding class and the log likelihood of the rest classes for discriminative training. The model parameters are estimated with the loss function using GPD(generalized probabilistic descent). For recognition performance comparison, we extracted the 12 degrees features using preprocessing and MFCC(mel-frequency cepstral coefficients) of the 9 kinds of environmental sounds and carry out GMM classification experiments. According to the experimental results, MCE training method showed the best performance by an average of 87.06% with 19 mixtures. This result confirmed us that MCE training method could be effectively used as a GMM training method in environmental sounds recognition.

Keywords

Context Aware;Environmental Sounds;GMM;MLE;Minimum Classification Error

Acknowledgement

Supported by : 한국연구재단

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