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http://dx.doi.org/10.7840/kics.2016.41.8.851

Statistical Voice Activity Detection Using Probabilistic Non-Negative Matrix Factorization  

Kim, Dong Kook (Chonnam National University, School of Electronic and Computer Engineering)
Shin, Jong Won (Gwangju Institute of Science and Technology, School of Electrical Engineering and Computer Science)
Kwon, Kisoo (Seoul National University, Department of Electrical and Computer Engineering and the Institute of New Media and Communications)
Kim, Nam Soo (Seoul National University, Department of Electrical and Computer Engineering and the Institute of New Media and Communications)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.41, no.8, 2016 , pp. 851-858 More about this Journal
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.
Keywords
voice activity detection; NMF; Poisson distribution; likelihood ratio test;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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