Speech Dereverberation by Constrained and Regularized Multi-channel Spectral Decomposition: Evaluated on Reverb Challenge

We present our contribution to the REVERB Challenge in this paper. A multi-channel speech dereverberation system combines cross-channel cancellation and spectral decomposition. The reverberation is modeled as a convolution operation in the spectral domain. Using the generalized Kullback-Leibler (KL) divergence, we decompose the reverberant magnitude spectrum into clean magnitude spectrum convolved with a deconvolution filter. The magnitude spectrum is constrained and regularized by non-negativity and sparsity, respectively, while the deconvolution filter is constrained by non-negativity and cross-channel cancellation. Spectral decomposition of individual channels and cross-channel cancellation are jointly optimized by a multiplicative algorithm to achieve multi-channel speech dereverberation. Experimental evaluations on “speech enhancement task” are carried out according to the evaluation guidelines of the REVERB challenge, showing promising results. The objective metrics for measuring reverberation are investigated through the algorithm evaluation.