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

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.