AR source modeling based on spatiotemporally diverse multichannel outputs and application to multimicrophone 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’s spatiotemporal 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 pure source dereverberation. A key parameter in this dereverberation scheme is the order of the source whitening filter. It determines the tradeoff between the modeling error (limited source whitening) and the estimation error (due to the blind estimation of the source correlations). In this paper we propose, using a statistical room reverberation model, a design to optimize the whitening order (function of the room characteristics, and the number of sub-channels).