IMPORTANCE OF SWITCH OPTIMIZATION CRITERION IN SWITCHING WPE DEREVERBERATION

Weighted prediction error (WPE) is a fundamental dereverberation method to predict the late reverberation component of an observed signal based on linear prediction (LP). Recently, WPE was extended to Switching WPE (SwWPE), which optimizes (i) multiple LP filters and (ii) switching parameters to determine the best LP filter used for each time-frequency bin. Conventionally, these parameters are optimized based on the maximum likelihood (ML) criterion, but this is not optimal in terms of signal quality, such as signal-to-distortion ratio (SDR) and word error rate (WER) of automatic speech recognition. We thus propose a new SwWPE processing flow that enables us to optimize switching parameters based on an arbitrary optimization criterion. Using oracle clean signals, we demonstrate the potential performance of our new approach with an SDR maximization criterion, revealing that it can significantly improve the SDR and WER obtained by the conventional ML-based SwWPE. This motivates us to propose new SwWPE processing in which the switching parameters are externally estimated using a deep neural network (DNN) that is trained with an end-to-end SDR maximization criterion. The experimental result clearly demonstrates the improved SDR performance of the new approach compared to the conventional WPE and SwWPE.