Distant Sound Source Suppression Based on Multichannel Nonnegative Matrix Factorization with Bases Distance Maximization Penalty

In this research, we address distant sound source suppression based on Multichannel Nonnegative Matrix Factorization (MNMF), and propose a new penalized method. A conventional method based on MNMF separates an observed signal into a target signal and other distant sound sources. Un-fortunately, MNMF often degrades the separation performance owing to the basis-sharing problem. Our penalized method forces the target basis to become different from the nontarget one. Experimental results show that the proposed method can improve the separation capability more than the conventional one.