A two-stage algorithm for noisy and reverberant speech enhancement

Abstract

In daily listening environments, speech is commonly corrupted by room reverberation and background noise. These distortions are detrimental to speech intelligibility and quality, and also severely degrade the performance of automatic speech and speaker recognition systems. In this paper, we propose a two-stage algorithm to deal with the confounding effects of noise and reverberation separately, where denoising and dereverberation are conducted sequentially using deep neural networks. In addition, we design a new objective function that incorporates clean phase information during training. As the objective function emphasizes more important time-frequency (T-F) units, better estimated magnitude is obtained during testing. By jointly training the two-stage model to optimize the proposed objective function, our algorithm improves objective metrics of speech intelligibility and quality significantly, and substantially outperforms one-stage enhancement baselines.