Speech dereverberation by data-dependent beamforming with signal pre-whitening

Among different microphone array processing techniques, data-dependent beamforming has been proven to be effective in suppressing ambient noise. When applied for dereverberation, however, the adaptation process results in a biased estimate of the beamformer coefficients leading to strong distortions at the beamformer output. In this paper, we investigate the origin of this bias for the generalized sidelobe canceller. It is shown that an unbiased estimate of the beam-former coefficients and thus dereverberation can be achieved if the source signal is a white random signal. Based on these findings, a pre-whitening approach for speech signals is proposed and combined with a generalized sidelobe canceller for speech dereverberation. The concept is demonstrated for the case of stationary speech-shaped noise as a source signal.