Abstract
This paper addresses the blind separation of convolutive and temporally correlated speech mixtures, through the use of a multichannel blind deconvolution (MBD) method. In the proposed method (NGA-LP) spatio-temporal separation is achieved by entropy maximization using the natural gradient algorithm (NGA), while a temporal prewhitening stage, based on linear prediction (LP), preserves the original spectral characteristics of each source contribution. It is further shown that a parameterized optimal nonlinearity derived from the generalized Gaussian density (GGD) model, increases the overall separation performance. Experiments with convolutive mixtures illustrate the merits of the proposed method.
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