This paper describes a novel algorithm for underdetermined speech separation problem based on compressed sensing which is an emerging technique for efficient data reconstruction. The proposed algorithm consists of two steps. The unknown mixing matrix is firstly estimated from the speech mixtures in the transform domain by using K-means clustering algorithm. In the second step, the speech sources are recovered based on an autocalibration sparse Bayesian learning algorithm for speech signal. Numerical experiments including the comparison with other sparse representation approaches are provided to show the achieved performance improvement.

Practical applications in digital communication need non-maximally decimated filter banks to provide oversampled baseband signals for other necessary operations. The non-maximally decimated filter banks allow the required prototype filter to have wider transitional band to significantly reduce the required computational complexity. This Letter presents a non-maximally decimated filter bank structure derived by using multi-rate signal flow graphs.

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