A New Approach to Underdetermined Blind Source Separation Using Sparse Representation

This paper presents a new approach to blind separation of sources using sparse representation in an underdetermined mixture. Firstly, we transform the observations into the new ones within the generalized spherical coordinates, through which the estimation of the mixing matrix is formulated as the estimation of the cluster centers. Secondly, we identify the cluster centers by a new classification algorithm, whereby the mixing matrix is estimated. The simulation results have shown the efficacy of the proposed algorithm.