- Volume 11 Issue 12
DOI QR Code
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
- Received : 2011.09.22
- Accepted : 2011.12.09
- Published : 2011.12.28
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.
Context Aware;Environmental Sounds;GMM;MLE;Minimum Classification Error
Supported by : 한국연구재단
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