Statistical Voice Activity Detection Using Probabilistic Non-Negative Matrix Factorization 


Vol. 41,  No. 8, pp. 851-858, Aug.  2016


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  Abstract

This paper presents a new statistical voice activity detection (VAD) based on the probabilistic interpretation of nonnegative matrix factorization (NMF). The objective function of the NMF using Kullback-Leibler divergence coincides with the negative log likelihood function of the data if the distribution of the data given the basis and encoding matrices is modeled as Poisson distributions. Based on this probabilistic NMF, the VAD is constructed using the likelihood ratio test assuming that speech and noise follow Poisson distributions. Experimental results show that the proposed approach outperformed the conventional Gaussian model-based and NMF-based methods at 0-15 dB signal-to-noise ratio simulation conditions.

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  Cite this article

[IEEE Style]

D. K. Kim, J. W. Shin, K. Kwon, N. S. Kim, "Statistical Voice Activity Detection Using Probabilistic Non-Negative Matrix Factorization," The Journal of Korean Institute of Communications and Information Sciences, vol. 41, no. 8, pp. 851-858, 2016. DOI: .

[ACM Style]

Dong Kook Kim, Jong Won Shin, Kisoo Kwon, and Nam Soo Kim. 2016. Statistical Voice Activity Detection Using Probabilistic Non-Negative Matrix Factorization. The Journal of Korean Institute of Communications and Information Sciences, 41, 8, (2016), 851-858. DOI: .

[KICS Style]

Dong Kook Kim, Jong Won Shin, Kisoo Kwon, Nam Soo Kim, "Statistical Voice Activity Detection Using Probabilistic Non-Negative Matrix Factorization," The Journal of Korean Institute of Communications and Information Sciences, vol. 41, no. 8, pp. 851-858, 8. 2016.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880