Single Channel Source Separation Using Smooth Nonnegative Matrix Factorization With Markov Random Fields
Minje Kim, Paris Smaragdis

This paper presents a single channel source separation method based on an extension of Nonnegative Matrix Factorization (NMF) algorithm by smoothing the original posterior probabilities with an additional Markov Random Fields (MRF) structure. Our method is based on the alternative interpretation of NMF with beta-divergence as latent variable models. By doing so, we can redefine NMF-based separation procedure as a Bayesian labeling problem where each label stands for the mask for a specific source. This understanding leads us to intervene in the calculation of posterior probabilities, so that the priors from MRF"s neighboring structure can smooth out isolated masking values that have different labeling results from their neighbors. Experiments on several dictionary-based source separation tasks show sensible performance gains.