Monaural Speech Separation Based on Computational Auditory Scene Analysis and Objective Quality Assessment of Speech

Monaural speech separation is a very challenging problem in speech signal processing. It has been studied previously, and many separation systems based on computational auditory scene analysis (CASA) have been proposed. Although the research on CASA has tended to introduce high level knowledge into separation process from primitive data-driven method, the knowledge of speech quality still has not been combined in it. In order to solve this problem, we proposed a new method which combined CASA with objective quality assessment of speech (OQAS). Through this combination, the performance of the speech separation can be improved not only in SNR, but also in mean opinion score (MOS)