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Underdetermined separation of speech mixture based on sparse Bayesian learning. (English) Zbl 1400.62118
Summary: 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.
MSC:
62H12 Estimation in multivariate analysis
62F15 Bayesian inference
62H30 Classification and discrimination; cluster analysis (statistical aspects)
Software:
BSS Eval; PDCO
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