Robust Speech-Distortion Weighted Interframe Wiener Filters for Single-Channel Noise Reduction

Abstract

In this paper, speech-distortion weighted (SDW) interframe Wiener filters (IFWFs) are investigated for single-channel noise reduction in a filter bank structure. The filters utilize a parameter $\mu$ that explicitly sets a tradeoff between noise reduction and speech distortion and have traditionally been used in multichannel applications under the term SDW multichannel Wiener filter. The application of these SDW-IFWFs relies on the estimation of interframe correlation (IFC) coefficients, and it is shown that the IFC coefficients can be more robustly estimated using a secondary higher resolution filter bank (HRFB). It is then shown how real-valued scalar gains, which are optimal in the primary filter bank, can be applied directly in the HRFB instead of the interframe filtering in the primary filter bank, which leads to a more robust noise reduction performance for any value of $\mu$ . Computing these gains is also cheaper since matrix inversions are avoided and the primary filter bank is not needed in the actual implementation. Experimental results are given that support the claims, where the proposed methods are compared to relevant reference methods using measures such as the segmental SNR and the objective PESQ.