Deep stacking networks with time series for speech separation

In many present speech separation approaches, the separation task is formulated as a binary classification problem. Several classification-based approaches have been proposed and performed satisfactorily. However, they do not explicitly model the correlation in time and each time-frequency (T-F) unit is still classified individually. As we know, the speech signal has a very rich time series and temporal dynamic information that can be exploited for speech separation. In this study, we incorporate the correlation in time into classification. Compared with the previous approaches, the proposed approach achieves better separation and generalization performance by using deep stacking networks (DSN) with time series and re-threshold method.