CONVOLUTIONAL WEIGHTED MINIMUM MEAN SQUARE ERROR FILTER FOR JOINT SOURCE SEPARATION AND DEREVERBERATION

Practical scenarios with multiple simultaneously active speakers recorded using one or more microphones in reverberant rooms pose a challenging problem when the extraction of the desired speaker signal is sought for. The majority of techniques found in the literature facilitate either source separation or dereverberation, which can at best be performed as subsequent, cascade processing. Recently, a solution to the joint task has been proposed, which is known as the weighted power minimization distortionless response (WPD) beamformer. In this paper, we derive a convolutional multichannel filter which performs jointly optimum dereverberation and desired source signal extraction. We formulate a single optimization criterion which minimizes the convolutional source-variance weighted mean square error (CW-MMSE), thereby effectively unifying the weighted prediction error (WPE) based dereverberation and MMSE filtering for the desired source extraction from reverberant mixtures of speakers. Experimental results show a significant performance improvement over the compared state-of-the-art methods such as WPD for datasets with simulated and recorded impulse responses.