Modified Complementary Joint Sparse Representations: A Novel Post-Filtering to MVDR Beamforming

Abstract

Post-filtering is a popular technique for multichannel speech enhancement system, in order to further improve the speech quality and intelligibility after beamforming. This paper presents a novel post-filtering to a minimum variance distortionless response (MVDR) beamforming which is a single-channel modified complementary joint sparse representations (M-CJSR) method. First, MVDR beamformer is used to suppress interference and noise. Subsequently, the proposed M-CJSR approach based on joint dictionary learning is applied as a single microphone post-filter to process the beamformer output. Different from the existing post-filtering techniques which rely on the assumptions about the noise field, this algorithm considers a more generalized signal model including the ambient noise, like diffuse noise or white noise, as well as the point-source interference. Moreover, the original CJSR method is extended to jointly learn dictionaries for not only the mappings from mixture to speech and noise, but also the mapping from mixture to interference. In order to take the complementary advantages of different sparse representations, we design the weighting parameters based on the residual components of the estimated signals. An experimental study which consists of objective evaluations under various conditions verifies the superiority of the proposed algorithm compared to other state-of-the-art methods.