Abstract
A block-based compressed sensing approach coupled with binary time-frequency masking is presented for the underdetermined speech separation problem. The proposed algorithm consists of multiple steps. First, the mixed signals are segmented to a number of blocks. For each block, the unknown mixing matrix is estimated in the transform domain by a clustering algorithm. Using the estimated mixing matrix, the sources are recovered by a compressed sensing approach. The coarsely separated sources are then used to estimate the time-frequency binary masks which are further applied to enhance the separation performance. The separated source components from all the blocks are concatenated to reconstruct the whole signal. Numerical experiments are provided to show the improved separation performance of the proposed algorithm, as compared with two recent approaches. The block-based operation has the advantage in improving considerably the computational efficiency of the compressed sensing algorithm without degrading its separation performance.