Online Speech Dereverberation Using Mixture of Multichannel Linear Prediction Models

We extend the state-of-the-art online dereverberation method, online weighted prediction error (WPE), which predicts late reverberation components using a multichannel linear prediction (MCLP) filter. The multi-input/output inverse theorem states that in general such an MCLP filter for WPE exists only if the number of sources is less than that of microphones M and there is no additive noise; otherwise, the WPE model has error, degrading its performance especially when M is small. To mitigate this WPE drawback, we recently developed an offline dereverberation method called switching WPE (SwWPE), which has multiple MCLP filters and switches them for each time-frequency bin. We here propose a recursive least squares (RLS) algorithm for the online optimization of SwWPE. Our proposed RLS has forgetting weights for each MCLP filter, and their decay rates are controlled based on a switching mechanism of SwWPE to stably improve the dereverberation performance of the online WPE. Experimental results show that our online SwWPE clearly outperforms online WPE in speech dereverberation tasks.