Subspace-based techniques for blind separation of convolutive mixtures with temporally correlated sources

This contribution addresses the blind identification of multiple input multiple output linear finite impulse response systems having a number of inputs less than the number of outputs. Recent publications have proposed an efficient second-order identification method in the single input multiple output case. Based on a subspace analysis, it allows a perfect recovery of the system parameters and excitation in a noise-free environment. Some extensions to the case of multiple inputs are also available under quite specific conditions. In this paper we indicate how to extend the original subspace-based approach to the general case.