A Fixed-Point Algorithm for Blind Separation of Temporally Correlated Sources
In this paper we develop a new method for blind separation of temporally correlated sources, possibly dependent signals from linear mixtures of them. The proposed algorithm is based on the mutual independency of the innovations of source signals instead of original signals. This algorithm takes into account both the temporal structure and the high-order statistics of source signals and in contrast to the most known blind separation algorithms only exploiting the second order statistics or the non-Gaussianity. In this framework, a fixed-point algorithm is introduced. The fixed-point algorithm is computationally very simple, converge fast, and does not need choose any learning step sizes. Extensive computer simulations with speech signals and images confirm the validity and high performance of the proposed algorithm.