Wiener filtering based speech enhancement with Weighted Denoising Auto-encoder and noise classification

Abstract

A novel speech enhancement method based on Weighted Denoising Auto-encoder (WDA) and noise classification is proposed in this paper. A weighted reconstruction loss function is introduced into the conventional Denoising Auto-encoder (DA), and the relationship between the power spectra of clean speech and noisy observation is described by WDA model. First, the sub-band power spectrum of clean speech is estimated by WDA model from the noisy observation. Then, the a priori SNR is estimated by the a Posteriori SNR Controlled Recursive Averaging (PCRA) approach. Finally, the clean speech is obtained by Wiener filter in frequency domain. In addition, in order to make the proposed method suitable for various kinds of noise conditions, a Gaussian Mixture Model (GMM) based noise classification method is employed. And the corresponding WDA model is used in the enhancement process. From the test results under ITU-T G.160, it is shown that, in comparison with the reference method which is the Wiener filtering method with decision-directed approach for SNR estimation, the WDA-based speech enhancement methods could achieve better objective speech quality, no matter whether the noise conditions are included in the training set or not. And the similar amount of noise reduction and SNR improvement can be obtained with smaller distortion on speech level.