Linear Prediction-Based Online Dereverberation and Noise Reduction Using Alternating Kalman Filters

Abstract

Multichannel linear prediction-based dereverberation in the short-time Fourier transform (STFT) domain has been shown to be highly effective. Using this framework, the desired dereverberated multichannel signal is obtained by filtering the noise-free reverberant signals using the estimated multichannel autoregressive (MAR) coefficients. To use such methods in the presence of noise, especially in the case of online processing, remains a challenging problem. Existing sequential enhancement structures, which first remove the noise and then estimate the MAR coefficients, suffer from a causality problem as both the optimal noise reduction and dereverberation stages depend on the current output of each other. To address this problem, an algorithm that consists of two alternating Kalman filters to estimate the noise-free reverberant signals and the (MAR) coefficients is proposed. The causality of the estimation procedure is important when dealing with time-variant acoustic scenarios, where the MAR coefficients are time-varying. The proposed method is evaluated using simulated and measured acoustic impulse responses and is compared to a method based on the same signal model. In addition, a method to control the reverberation reduction and noise reduction independently is derived.