Blind separation of sources, Part II: problems statement
Signal Processing
Adapted wavelet analysis from theory to software
Adapted wavelet analysis from theory to software
A neural learning algorithm for blind separation of sources based on geometric properties
Signal Processing - Special issue on neural networks
Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Blind Source Separation by Sparse Decomposition in a Signal Dictionary
Neural Computation
Learning Overcomplete Representations
Neural Computation
Blind separation of mixture of independent sources through aquasi-maximum likelihood approach
IEEE Transactions on Signal Processing
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Image compression via joint statistical characterization in the wavelet domain
IEEE Transactions on Image Processing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Blind separation of convolutive image mixtures
Neurocomputing
Analysis and segmentation of brain MR images with blind-source-separation techniques
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Blind source separation using mixtures scatter plot properties
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Robust shape and polarisation estimation using blind source separation
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Improvement on multivariate statistical process monitoring using multi-scale ICA
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Sparse deflations in blind signal separation
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Robust estimation of shape and polarisation using blind source separation
Pattern Recognition Letters
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We consider the problem of blind separation of unknown source signals or images from a given set of their linear mixtures. It was discovered recently that exploiting the sparsity of sources and their mixtures, once they are projected onto a proper space of sparse representation, improves the quality of separation. In this study we take advantage of the properties of multiscale transforms, such as wavelet packets, to decompose signals into sets of local features with various degrees of sparsity. We then study how the separation error is affected by the sparsity of decomposition coefficients, and by the misfit between the probabilistic model of these coefficients and their actual distribution. Our error estimator, based on the Taylor expansion of the quasi-ML function, is used in selection of the best subsets of coefficients and utilized, in turn, in further separation. The performance of the algorithm is evaluated by using noise-free and noisy data. Experiments with simulated signals, musical sounds and images, demonstrate significant improvement of separation quality over previously reported results.