Blind Separation of Linear-Quadratic Mixtures of Mutually Independent and Autocorrelated Sources

In this paper, we are interested in the blind separation of linear-quadratic mixtures of mutually independent sources when successive samples of each source are correlated. When a linear source separation method based on second-order statistics, like the well-known AMUSE method, is applied to this type of mixture, it provides subclasses of the initial mixture where each source can remain mixed with its square. We propose a new approach to then separate these two components. Simulations show the very good performance of our method, as compared with two other methods.