Iterated delay and predict equalization for blind speech dereverberation

In this paper, we consider the blind multichannel dereverberation problem for a single source. The multichannel reverberation impulse response is assumed to be stationary enough to allow estimation of the correlations it induces from the received signals. It is well-known that a single-input multi-output (SIMO) filter can be equalized blindly by applying multichannel linear prediction (LP) to its output when the input is white. When the input is colored, the multichannel linear prediction will both equalize the reverberation filter and whiten the source. We exploit the channel spatial diversity to estimate the source correlation structure, which can hence be used to determine a source whitening filter. Multichannel linear prediction is then applied to the sensor signals filtered by the source whitening filter, to obtain source dereverberation. We exploit the input signal time diversity to reduce the equalization noise. Due to the speech signal non-stationarity, averaging equalizers (which are computed on different frames) increases the dereverberation accuracy. Simulation results reveal that an iterated equalization scheme (based on frame-by-frame analysis) increases the dereverberation performance, and leads to better auditive results.