Estimating Ar Parameter-sets for Linear-recurrent Signals in Convolutive Mixtures

This article investigates a theoretical basis for estimating autoregressive (AR) processes for linear-recurrent signals in convolutive mixtures. Whitening of such signals is sometimes a problem in multichannel blind equalization which is intended to extract the original signals even if the signals are of a convolutive mixture type. This whitening is due to inverse-filtering which deconvolves the AR processes that generate the linear-recurrent signals. To avoid this excessive deconvolution, it is effective to remove the contributions of such processes from the inverse-filters. Unfortunately, no method seems to have been able to extract the AR parameter-sets for respective signals included in convolutive mixtures.