Second-order blind separation of sources based on canonical partial innovations

This paper is devoted to the study of second-order properties using partial autocorrelations of an instantaneous mixture of colored sources without additive noise. We introduce the notion of symmetric recursive canonical partial innovation. Then, their components, for the observation process, meet exactly with those of the source process from the order for which the autoregressive models underlying the sources are distinct. This property leads to a new separation method based on the sample counterpart of partial autocorrelation matrices associated with these innovations. Simulation results show a notable improvement of the achievements of such an approach with respect to those of similar methods. Tno other separation methods related to partial autocorrelation are also discussed.