Weibull Prior based Single Channel Speech Enhancement using Iterative Posterior NMF

Abstract

This paper proposes a speech enhancement method for non-stationary Gaussian noise based on regularized non-negative matrix factorization (NMF). The magnitudes of speech and noise are implemented by a model based in iterative posterior NMF which are applied using prior distributions in transform domain. This is used since the sample distributions of the above are well suited to Weibull and Rayleigh densities well. For the accomplishment in time-varying noise environments, both the speech and noise bases of NMF are adapted simultaneously. With the usage of estimated speech presence probability, this paper proposes to adaptively estimate the statistics of these distributions. The method in this paper gives the best results for perceptual evaluation of speech quality (PESQ) and the signal-to-distortion ratio (SDR).