Virtual acoustic channel expansion based on neural networks for weighted prediction error-based speech dereverberation

Abstract

In this study, we propose a neural-network-based virtual acoustic channel expansion (VACE) framework for weighted prediction error (WPE)-based speech dereverberation. Specifically, for the situation in which only a single microphone observation is available, we aim to build a neural network capable of generating a virtual signal that can be exploited as the secondary input for the dual-channel WPE algorithm, thus making its dereverberation performance superior to the single-channel WPE. To implement the VACE-WPE, the neural network for the VACE is initialized and integrated to the pre-trained neural WPE algorithm. The entire system is then trained in a supervised manner to output a dereverberated signal that is close to the oracle early arriving speech. Experimental results show that the proposed VACE-WPE method outperforms the single-channel WPE in a real room impulse response shortening task.