Nonnegative matrix factorization 2D with the flexible β-Divergence for Single Channel Source Separation

This paper presents an algorithm for nonnegative matrix factorization 2D (NMF-2D) with the flexible beta-Divergence. The beta-Divergence is a group of cost functions parametrized by a single parameter beta. The Least Squares divergence, Kullback-Leibler divergence and the Itakura-Saito divergence are special cases (beta=2,1,0). This paper presents a more complete algorithm which uses a flexible range of beta, instead of be limited to just special cases. We describe a maximization-minimization (MM) algorithm lead to multiplicative updates. The proposed factorization decomposes an information-bearing matrix into two-dimensional convolution of factor matrices that represent the spectral dictionary and temporal codes with enhanced performance. The method is demonstrated on the separation of audio mixtures recorded from a single channel. Experimental tests and comparisons with other factorization methods have been conducted to verify the efficacy of the proposed method.