Space or time adaptive signal processing by neural network models
AIP Conference Proceedings 151 on Neural Networks for Computing
Pattern Recognition Letters
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
Variational and stochastic inference for Bayesian source separation
Digital Signal Processing
Separating more sources than sensors using time-frequency distributions
EURASIP Journal on Applied Signal Processing
K-hyperline clustering learning for sparse component analysis
Signal Processing
Underdetermined blind source separation based on subspace representation
IEEE Transactions on Signal Processing
Underdetermined blind source separation based on relaxed sparsity condition of sources
IEEE Transactions on Signal Processing
An iterative Bayesian algorithm for sparse component analysis in presence of noise
IEEE Transactions on Signal Processing
Blind underdetermined mixture identification by joint canonical decomposition of HO cumulants
IEEE Transactions on Signal Processing
Blind Identification of Underdetermined Mixtures by Simultaneous Matrix Diagonalization
IEEE Transactions on Signal Processing
Fourth-Order Cumulant-Based Blind Identification of Underdetermined Mixtures
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
Underdetermined Blind Separation of Nondisjoint Sources in the Time-Frequency Domain
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
Blind source separation based on time-frequency signalrepresentations
IEEE Transactions on Signal Processing
Convolutive Blind Source Separation in the Frequency Domain Based on Sparse Representation
IEEE Transactions on Audio, Speech, and Language Processing
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Sparse component analysis and blind source separation of underdetermined mixtures
IEEE Transactions on Neural Networks
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To estimate precisely the mixing matrix and extract the source signals in underdetermined case is a challenging problem, especially when the source signals are non-disjointed in time-frequency (TF) domain. The conventional algorithms such as subspace-based achieve blind source separation exploiting the sparsity of the original signals and the mixtures must satisfy the assumption that the number of sources that contribute their energy at any TF point is strictly less than that of sensors. This paper proposes a new method considering the uncorrelated property of the sources in the practical field which relaxes the sparsity condition of sources in TF domain. The method shows that the number of the sources that exist in any TF neighborhood simultaneously equals to that of sensors. We can identify the active sources and estimate their corresponding TF values in any TF neighborhood by matrix diagonalization. Moreover, this paper proposes a method for estimating the mixing matrix by classifying the eigenvectors corresponded to the single source TF neighborhoods. The simulation results show the proposed algorithm separates the sources with higher signal-to-interference ratio compared to other conventional algorithms.