Adaptive subspace algorithm for blind separation of independent sources in convolutive mixture

We propose an algorithm for blind separation of sources in convolutive mixtures based on a subspace approach. The advantage of this algorithm is that it reduces a convolutive mixture to an instantaneous mixture by using only second-order statistics (but more sensors than sources). Furthermore, the sources can be separated by using any algorithm for an instantaneous mixture (based generally on fourth-order statistics). Otherwise, the classical assumptions for blind separation of sources (at most one source can be a Gaussian signal and the sources are statistically independent) and some new subspace assumptions are considered. The assumptions concern the subspace model and their properties are emphasized. Finally, an experimental study is conducted and results are discussed.