Multi-Channel Joint Dereverberation and Denoising using Deep Priors

Reverberation and ambient noise present in an audio scene degrades the speech intelligibility and perceptual quality of speech based query applications. The problem of joint speech dereverberation and denoising is challenging when compared to sequential dereverberation and denoising. In this paper this joint problem is solved by a using a model-based optimization technique for dereveberation and a corresponding DNN with deep priors for the denoising part. This joint enhancement algorithm is then applied to every channel in a multi-channel scenario. The processed outputs of every channel are then combined using beamforming method to compute a spatially filtered signal. This method therefore utilities both spectral and beam forming techniques for speech enhancement in a multi channel scenario. Subjective, objective Word error rate evaluations indicate a significant improvement under both noisy and reverberant conditions.