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http://dx.doi.org/10.5762/KAIS.2012.13.2.817

Estimation of Optimal Mixture Number of GMM for Environmental Sounds Recognition  

Han, Da-Jeong (Division of Electronic and Computer Engineering, Chonnam University)
Park, Aa-Ron (Division of Electronic and Computer Engineering, Chonnam University)
Baek, Sung-June (Division of Electronic and Computer Engineering, Chonnam University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.13, no.2, 2012 , pp. 817-821 More about this Journal
Abstract
In this paper we applied the optimal mixture number estimation technique in GMM(Gaussian mixture model) using BIC(Bayesian information criterion) and MDL(minimum description length) as a model selection criterion for environmental sounds recognition. In the experiment, we extracted 12 MFCC(mel-frequency cepstral coefficients) features from 9 kinds of environmental sounds which amounts to 27747 data and classified them with GMM. As mentioned above, BIC and MDL is applied to estimate the optimal number of mixtures in each environmental sounds class. According to the experimental results, while the recognition performances are maintained, the computational complexity decreases by 17.8% with BIC and 31.7% with MDL. It shows that the computational complexity reduction by BIC and MDL is effective for environmental sounds recognition using GMM.
Keywords
Gaussian mixture model; BIC; MDL; Bayesian information criterion; environmental sounds recognition;
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