Blind Signal Dereverberation Based on Mixture of Weighted Prediction Error Models

Abstract

We extend the linear prediction-based dereverberation method called weighted prediction error (WPE). WPE optimizes a causal finite impulse response (FIR) filter that predicts the late reverberation components of an observed signal. However, by the multi-input/output inverse (MINT) theorem, in general, such FIR filters exist only when the number of sources is fewer than that of the microphones and no ambient noise exists. To mitigate the model error of WPE in adverse environments, we propose a mixture model of multiple WPEs in which the time frames are divided into clusters in each frequency bin and the WPE's causal FIR filter is optimized in each cluster. Experimental results show that our proposed method significantly improves the dereverberation performance of WPE when the noise level is high or the number of microphones is small.