Optimized Speech Dereverberation From Probabilistic Perspective for Time Varying Acoustic Transfer Function

摘要

A dereverberation technique has been developed that optimally combines multichannel inverse filtering (MIF), beamforming (BF), and non-linear reverberation suppression (NRS). It is robust against acoustic transfer function (ATF) fluctuations and creates less distortion than the NRS alone. The three components are optimally combined from a probabilistic perspective using a unified likelihood function incorporating two probabilistic models. A multichannel probabilistic source model based on a recently proposed local Gaussian model (LGM) provides robustness against ATF fluctuations of the early reflection. A probabilistic reverberant transfer function model (PRTFM) provides robustness against ATF fluctuations of the late reverberation. The MIF and multichannel under-determined source separation (MUSS) are optimized in an iterative manner. The MIF is designed to reduce the time-invariant part of the late reverberation by using optimal time-weighting with reference to the PRTFM and the LGM. The MUSS separates the dereverberated speech signal and the residual reverberation after the MIF, which can be interpreted as an optimized combination of the BF and the NRS. The parameters of the PRTFM and the LGM are optimized based on the MUSS output. Experimental results show that the proposed method is robust against the ATF fluctuations under both single and multiple source conditions.