Efficient multichannel nonnegative matrix factorization exploiting rank-1 spatial model

This paper proposes a new efficient multichannel nonnegative matrix factorization (NMF) method. Recently, multichannel NMF (MNMF) has been proposed as a means of solving the blind source separation problem. This method estimates a mixing system of sources and attempts to separate them in a blind fashion. However, this method is strongly dependent on its initial values because there are no constraints in the spatial models. To solve this problem, we introduce a rank-1 spatial model into MNMF. The proposed method estimates a demixing matrix while representing sources using NMF bases and can be optimized by the update rules of independent vector analysis and conventional single-channel NMF. Experimental results show the efficacy of the proposed method in terms of robustness and convergence speed.