Applied numerical linear algebra
Applied numerical linear algebra
Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
The Geometry of Algorithms with Orthogonality Constraints
SIAM Journal on Matrix Analysis and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Blind Source Separation by Sparse Decomposition in a Signal Dictionary
Neural Computation
Blind separation of disjoint orthogonal signals: demixing N sources from 2 mixtures
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 05
A Novel Subspace Clustering Method for Dictionary Design
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
A Uniform Framework for Ad-Hoc Indexes to Answer Reachability Queries on Large Graphs
DASFAA '09 Proceedings of the 14th International Conference on Database Systems for Advanced Applications
A fast approach for overcomplete sparse decomposition based on smoothed l0 norm
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Probing the Pareto Frontier for Basis Pursuit Solutions
SIAM Journal on Scientific Computing
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
SIAM Journal on Imaging Sciences
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
SIAM Journal on Imaging Sciences
Subspace pursuit for compressive sensing signal reconstruction
IEEE Transactions on Information Theory
Model-based expectation-maximization source separation and localization
IEEE Transactions on Audio, Speech, and Language Processing
Double sparsity: learning sparse dictionaries for sparse signal approximation
IEEE Transactions on Signal Processing
A robust method to count and locate audio sources in a multichannel underdetermined mixture
IEEE Transactions on Signal Processing
Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation
A multistage approach to blind separation of convolutive speech mixtures
Speech Communication
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
Blind separation of speech mixtures via time-frequency masking
IEEE Transactions on Signal Processing
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Blind identification and source separation in 2×3 under-determined mixtures
IEEE Transactions on Signal Processing
Performance measurement in blind audio source separation
IEEE Transactions on Audio, Speech, and Language Processing
Greed is good: algorithmic results for sparse approximation
IEEE Transactions on Information Theory
On the exponential convergence of matching pursuits in quasi-incoherent dictionaries
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
IEEE Transactions on Image Processing
Noise variance estimation based on dual-channel phase difference for speech enhancement
Digital Signal Processing
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A block-based approach coupled with adaptive dictionary learning is presented for underdetermined blind speech separation. The proposed algorithm, derived as a multi-stage method, is established by reformulating the underdetermined blind source separation problem as a sparse coding problem. First, the mixing matrix is estimated in the transform domain by a clustering algorithm. Then a dictionary is learned by an adaptive learning algorithm for which three algorithms have been tested, including the simultaneous codeword optimization (SimCO) technique that we have proposed recently. Using the estimated mixing matrix and the learned dictionary, the sources are recovered from the blocked mixtures by a signal recovery approach. The separated source components from all the blocks are concatenated to reconstruct the whole signal. The block-based operation has the advantage of improving considerably the computational efficiency of the source recovery process without degrading its separation performance. Numerical experiments are provided to show the competitive separation performance of the proposed algorithm, as compared with the state-of-the-art approaches. Using mutual coherence and sparsity index, the performance of a variety of dictionaries that are applied in underdetermined speech separation is compared and analyzed, such as the dictionaries learned from speech mixtures and ground truth speech sources, as well as those predefined by mathematical transforms such as discrete cosine transform (DCT) and short time Fourier transform (STFT).