Group sparsity for mimo speech dereverberation

Reverberation can severely affect the speech signals recorded in a room, possibly leading to a significantly reduced speech quality and intelligibility. In this paper we present a batch algorithm employing a signal model based on multi-channel linear prediction in the short-time Fourier transform domain. Aiming to achieve multiple-input multiple-output (MIMO) speech dereverberation in a blind manner, we propose a cost function based on the concept of group sparsity. To minimize the obtained nonconvex function, an iteratively reweighted least-squares procedure is used. Moreover, it can be shown that the derived algorithm generalizes several existing speech dereverberation algorithms. Experimental results for several acoustic systems demonstrate the effectiveness of nonconvex sparsity-promoting cost functions in the context of dereverberation.