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http://dx.doi.org/10.5392/JKCA.2011.11.12.497

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

Han, Da-Jeong (전남대학교 전자컴퓨터공학부)
Park, Aa-Ron (전남대학교 전자컴퓨터공학부)
Park, Jun-Qyu (전남대학교 전자컴퓨터공학부)
Baek, Sung-June (전남대학교 전자컴퓨터공학부)
Publication Information
The Journal of the Korea Contents Association / v.11, no.12, 2011 , pp. 497-503 More about this Journal
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;
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Times Cited By KSCI : 2  (Citation Analysis)
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