Non negative sparse representation for Wiener based source separation with a single sensor

Laurent Benaroya, Lorcan Mc Donagh, Frédéric Bimbot, Rémi Gribonval

Abstract: We propose a new method to perform the separation of two sound sources from a single sensor. This method generalizes Wiener filtering with locally stationary, non-Gaussian, parametric source models. The method involves a learning phase for which we propose three different algorithm. In the separation phase, we use a sparse non negative decomposition algorithm of our own. The algorithms are evaluated on the separation of real audio data.