Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Flexible Independent Component Analysis
Journal of VLSI Signal Processing Systems
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Analysis of sparse representation and blind source separation
Neural Computation
Blind Source Separation by Sparse Decomposition in a Signal Dictionary
Neural Computation
A Variational Method for Learning Sparse and Overcomplete Representations
Neural Computation
Learning Overcomplete Representations
Neural Computation
Variational and stochastic inference for Bayesian source separation
Digital Signal Processing
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
Second-order blind separation of sources based on canonical partialinnovations
IEEE Transactions on Signal Processing
Blind separation of speech mixtures via time-frequency masking
IEEE Transactions on Signal Processing
Sparse signal reconstruction from limited data using FOCUSS: are-weighted minimum norm algorithm
IEEE Transactions on Signal Processing
Underdetermined Blind Separation of Nondisjoint Sources in the Time-Frequency Domain
IEEE Transactions on Signal Processing
Blind separation of instantaneous mixtures of nonstationary sources
IEEE Transactions on Signal Processing
Underdetermined blind source separation based on sparse representation
IEEE Transactions on Signal Processing
Bayesian blind separation of generalized hyperbolic processes in noisy and underdeterminate mixtures
IEEE Transactions on Signal Processing
| Hi-index | 35.68 |
This paper considers the problem of blindly separating sub- and super-Gaussian sources from underdetermined mixtures. The underlying sources are assumed to be composed of two orthogonal components: one lying in the rowspace and the other in the nullspace of a mixing matrix. The mapping from the rowspace component to the mixtures by the mixing matrix is invertible using the pseudo-inverse of the mixing matrix. The mapping from the nullspace component to zero by the mixing matrix is noninvertible, and there are infinitely many solutions to the nullspace component. The latent nullspace component, which is of lower complexity than the underlying sources, is estimated based on a mean square error (MSE) criterion. This leads to a source estimator that is optimal in the MSE sense. In order to characterize and model sub- and super-Gaussian source distributions, the parametric generalized Gaussian distribution is used. The distribution parameters are estimated based on the expectation-maximization (EM) algorithm. When the mixing matrix is unavailable, it must be estimated, and a novel algorithm based on a single source detection algorithm, which detects time-frequency regions of single-source-occupancy, is proposed. In our simulations, the proposed algorithm, compared to other conventional algorithms, estimated the mixing matrix with higher accuracy and separated various sources with higher signal-to-interference ratio.