Adaptive Speech Dereverberation Using Constrained Sparse Multichannel Linear Prediction

Abstract

In this letter, we present an adaptive speech dereverberation method based on constrained sparse multichannel linear prediction (MCLP), minimizing the mixed ℓ2,p norm of the desired component. In order to prevent overestimation of the undesired reverberant component, possibly leading to severe distortions of the output, we propose to use a statistical model for late reverberation to limit the power of the MCLP-based estimate. The resulting constrained optimization problem is solved by using the alternating direction method of multipliers, resulting in two variants of the dereverberation algorithm. Simulation results show that the proposed constraint increases the robustness with respect to parameter selection and improves the usability for dynamic scenarios in comparison to the unconstrained method.