Integrating binary mask estimation with MRF priors of cochleagram for speech separation

In present binary masking based speech separation systems, it is almost impossible to obtain the ideal binary mask (IBM). The error in IBM estimation usually results in energy absence in many speech-dominated time-frequency (T-F) units. It violates smooth evolution nature of the speech signal and creates great artefacts. Markov random field (MRF) is one of the promising approaches to model smooth evolution nature which has been extensively applied to image smoothing applications. In this letter, an MRF prior for modeling the spatial dependencies in audio cochleagram is introduced. With this prior model, we further smooth the binary mask based cochleagram and generalize binary mask to ratio mask via a Bayesian framework. Our algorithm is systematically evaluated and compared with other counterpart methods, and it yields substantially better performance, especially on suppressing artefacts.