NDLI: An improved Bayesian NMF-based speech enhancement method using multivariate Laplace distribution

Content Provider	IEEE Xplore Digital Library
Author	Liwei Zhang Xiongwei Zhang Xia Zou Gang Min
Copyright Year	2014
Description	Author affiliation: PLA Univ. of Sci. & Tech., Nanjing, China (Liwei Zhang; Xiongwei Zhang; Xia Zou; Gang Min)
Abstract	Bayesian NMF (BNMF) algorithm joints nonnegative matrix factorization (NMF) with a statistical framework, and performs well in speech enhancement. However, the dependencies of atoms in speech frame are not considered in the method. In order to exploit the dependencies of the speech and noise signals, we introduce multivariate Laplace distribution for the basis W and NMF coefficients matrix H. In this paper, we propose a novel speech enhancement method, which is based on an improved Bayesian NMF (IBNMF) algorithm using multivariate Laplace distribution. The experimental results show that the proposed algorithm yields improvements in Log-spectral distance (LSD) and Perceptual Evaluation of Speech Quality (PESQ), compared to the other two algorithms, which are based on NMF and BNMF methods.
Starting Page	1
Ending Page	6
File Size	1098013
Page Count	6
File Format	PDF
ISBN	9781479973392
DOI	10.1109/WCSP.2014.6992007
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Noise Signal processing algorithms Speech enhancement Speech Multivariate Laplace distribution Bayes methods Random variables Noise measurement Improved BNMF
Content Type	Text
Resource Type	Article

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