A psychoacoustically motivated speech distortion weighted multi-channel wiener filter for noise reduction
Bruno Defraene, Kim Ngo, Toon van Waterschoot, Moritz Diehl, Marc Moonen · 2012
The aim of this paper is to improve the performance of existing speech distortion weighted multi-channel Wiener filter (SDW-MWFμ) based noise reduction (NR) algorithms. It is well known that for the SDW-MWFμthe improved NR performance comes at the cost of higher speech distortion when a fixed speech distortion weighting factor is used. In this paper we propose two psychoacoustically motivated weighting factor selection strategies, devised to exploit masking properties of the human ear. Experimental results based on PESQ scores, SNR improvement, and signal distortion confirm that both proposed psychoacoustically motivated weighting factor selection strategies do improve the NR performance compared to using a fixed weighting factor. In some of the analyzed scenarios, the fixed weighting factor approach is even seen to degrade the PESQ scores, while the psychoacoustically motivated approaches are seen to significantly improve the PESQ scores in all of the analyzed scenarios.