Dereverberation and denoising using multichannel linear prediction

Reverberation in a room severely degrades the characteristics and auditory quality of speech captured by distant microphones, thus posing a severe problem for many speech applications. Several dereverberation techniques have been proposed with a view to solving this problem. There are, however, few reports of dereverberation methods working under noisy conditions. In this paper, we propose an extension of a dereverberation algorithm based on multichannel linear prediction that achieves both the dereverberation and noise reduction of speech in an acoustic environment with a colored noise source. The method consists of two steps. First, the speech residual is estimated from the observed signals by employing multichannel linear prediction. When we use a microphone array, and assume, roughly speaking, that one of the microphones is closer to the speaker than the noise source, the speech residual is unaffected by the room reverberation or the noise. However, the residual is degraded because linear prediction removes an average of the speech characteristics. In a second step, the average of the speech characteristics is estimated and used to recover the speech. Simulations were conducted for a reverberation time of 0.5 s and an input signal-to-noise ratio of 0 dB. With the proposed method, the reverberation was suppressed by more than 20 dB and the noise level reduced to -18 dB.