UNDERDETERMINED BLIND SOURCE SEPARATION USING A PROBABILISTIC SOURCE SPARSITY MODEL
Luis Vielva, Deniz Erdoğmuş, José Carlos Príncipe · 2001
Blind source separation consists of recovering # source signals from # measurements that are an unknown function of the sources. In solving the underdetermined (###) linear problem three stages can be identified: to represent the signals in an appropriate domain, to estimate the mixing matrix, and to invert the linear problem to estimate the sources. As a consequence of having more degrees of freedom than constraints, the inverse problem has an infinite number of solutions. To choose the "best" solution, additional constraints have to be imposed on the basis of some performance criterion or previous knowledge. In this communication we present a method that choose the "best" demixing matrix in a sample by sample basis by using some previous knowledge of the statistics of the sources. The behaviour of the estimator is compared to the global pseudo inverse approach and with other local heuristic methods by means of Montecarlo simulations.