Joint Beamforming And Reverberation Cancellation Using A Constrained Kalman Filter With Multichannel Linear Prediction

The performance of speech processing systems degrades significantly in far-field scenarios where the distance between the user and microphones increases, leading to low signal-to-noise and signal-to-reverberation ratios. To address this challenge, combining the denoising and dereverberation techniques in both parallel and cascade configurations has been widely studied. However, a parallel or cascade combination may not be efficient while imposing a large computational complexity. We propose a constrained Kalman filter based multichannel linear prediction method to jointly perform denoising and dereverberation efficiently using an online processing algorithm. In contrast to previously proposed methods which utilize steering vectors based on the relative early transfer function, our algorithm is implemented using a direct relative transfer function based steering vector, which aims at extracting the direct sound as opposed to preserving the early reflections. We show that the proposed algorithm outperforms existing online implementations of integrated beamformer and linear prediction methods on the REVERB challenge speech enhancement task while being computationally less complex.