A Natural Gradient Convolutive Blind Source Separation Algorithm for Speech Mixtures

In this paper, a novel algorithm for separating mixtures of multiple speech signals measured by multiple microphones in a room environment is proposed. The algorithm is a modification of an existing approach for density-based multichannel blind deconvolution using natural gradient adaptation. It employs linear predictors within the coefficient updates and produces separated speech signals whose autocorrelation properties can be arbitrarily specified. Stationary point analyses of the proposed method illustrate that, unlike multichannel blind deconvolution methods, the proposed algorithm maintains the spectral content of the original speech signals in the extracted outputs. Performance comparisons of the proposed method with existing techniques show its desirable properties in separating real-world speech mixtures.