Time-Deconvolutive CNMF for Multichannel Blind Source Separation

This paper tackles multichannel separation of convolutive mixtures of audio sources by using complex-valued nonnegative matrix factorization (CNMF). We extend models proposed by previous works and show that one may tailor advanced single-channel NMF techniques, such as the deconvolutive NMF, to the multichannel factorization scheme. Additionally, we propose a regularized cost function that enables the user to control the distribution of the estimated parameters without significantly increasing the underlying computational cost. We also develop an optimization framework compatible with previous related works. Our simulations show that the proposed deconvolutive model offers advantages when compared to the simple NMF, and that the regularization is able to steer the parameters towards a solution with desirable properties. Keywords— Blind source separation, convolutive mixture, NMF, deconvolutive NMF