Monaural speech separation system based on optimum soft mask

The ideal binary mask (IBM) has been one of the most successful techniques in computational auditory scene analysis (CASA) algorithms. The binary value 1 is assigned to the mask if the local signal-to-noise ratio (SNR) of a particular time-frequency (T-F) units exceeds the local criterion (LC), otherwise the value 0 is assigned to the mask. This binary weighting may discard some parts of the speech during synthesis, which leads to an unnatural sound called musical noise. This paper proposes the optimum soft mask (OSM) to reduce the musical noise, by replacing the hard limiting weights (i.e., 1 or 0) with the variable weights between 0 and 1. The Signal-to-Noise ratio is used as a performance measures to compare the performance of the proposed soft mask with IBM in the context of monaural speech separation. The experimental results show the superior performance of the proposed soft mask as compared with IBM.