20,408 research outputs found
Sparse Bayesian Learning Approach for Discrete Signal Reconstruction
This study addresses the problem of discrete signal reconstruction from the
perspective of sparse Bayesian learning (SBL). Generally, it is intractable to
perform the Bayesian inference with the ideal discretization prior under the
SBL framework. To overcome this challenge, we introduce a novel discretization
enforcing prior to exploit the knowledge of the discrete nature of the
signal-of-interest. By integrating the discretization enforcing prior into the
SBL framework and applying the variational Bayesian inference (VBI)
methodology, we devise an alternating update algorithm to jointly characterize
the finite alphabet feature and reconstruct the unknown signal. When the
measurement matrix is i.i.d. Gaussian per component, we further embed the
generalized approximate message passing (GAMP) into the VBI-based method, so as
to directly adopt the ideal prior and significantly reduce the computational
burden. Simulation results demonstrate substantial performance improvement of
the two proposed methods over existing schemes. Moreover, the GAMP-based
variant outperforms the VBI-based method with an i.i.d. Gaussian measurement
matrix but it fails to work for non i.i.d. Gaussian matrices.Comment: 13 pages, 7 figure
Underdetermined Separation of Speech Mixture Based on Sparse Bayesian Learning
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
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