The multi-channel linear prediction framework for blind speech dereverberation has gained increased popularity over the recent years. While adaptive dereverberation is desirable, most multichannel linear prediction algorithms are based on either batch or iterative frame-by-frame processing, where individual frames are treated independently. In this paper, we derive a partitioned block frequency domain Kalman filter that offers adaptive processing. The so-called excessive whitening problem is avoided by including an estimate of the target speech signal coloration in the filter update. The impact of constraining the state covariance matrix is discussed. The convergence behavior of the algorithm is evaluated in terms of the evolution of the room acoustical parameters direct-to-reverberant ratio, clarity index and early decay time, indicating good dereverberation performance.
['T. Bietzen', 'A. Spriet', 'Wouter Tirry', 'Simon Doclo', 'Marc Moonen', 'T. van Waterschoot']
Partitioned block frequency domain Kalman filter for multi-channel linear prediction based blind speech dereverberation