Plug-and-Play MVDR Beamforming for Speech Separation

As an adaptive beamformer, the Minimum Variance Distortionless Response (MVDR) method has proven its efficiency in separating target speech from background noise and interference. Conventionally, MVDR relies on physical information regarding signal angles and covariance matrices, however, ignores that the beamformer output can potentially benefit from the prior structures of speech signals. Motivated by the recent advance in integrating physics-based and data-driven approaches, this paper introduces a novel speech separation framework. Our approach enhances MVDR by incorporating Plug-and-Play (PnP) techniques to capture speech priors, specifically employing the Regularization by Denoising (RED) method to integrate prior speech information obtained from data into the optimization process. Experimental results validate the effectiveness of the proposed approach.