Elements of information theory
Elements of information theory
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Adaptive blind separation of independent sources: a deflation approach
Signal Processing
A fast fixed-point algorithm for independent component analysis
Neural Computation
Natural gradient works efficiently in learning
Neural Computation
High-order contrasts for independent component analysis
Neural Computation
Natural gradient learning for over- and under-complete bases in ICA
Neural Computation
On the Stability of Source Separation Algorithms
Journal of VLSI Signal Processing Systems
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
Subspace Information Criterion for Model Selection
Neural Computation
Online Model Selection Based on the Variational Bayes
Neural Computation
Nonholonomic Orthogonal Learning Algorithms for Blind Source Separation
Neural Computation
Learning Overcomplete Representations
Neural Computation
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
General approach to blind source separation
IEEE Transactions on Signal Processing
Source separation using a criterion based on second-orderstatistics
IEEE Transactions on Signal Processing
Sequential blind extraction of instantaneously mixed sources
IEEE Transactions on Signal Processing
Nonsymmetrical contrasts for sources separation
IEEE Transactions on Signal Processing
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
Blind separation of instantaneous mixtures of nonstationary sources
IEEE Transactions on Signal Processing
A class of neural networks for independent component analysis
IEEE Transactions on Neural Networks
A robust approach to independent component analysis of signals with high-level noise measurements
IEEE Transactions on Neural Networks
An Adaptive Method for Subband Decomposition ICA
Neural Computation
Blind Source Separation Coping with the Change of the Number of Sources
Neural Information Processing
Blind source separation with dynamic source number using adaptive neural algorithm
Expert Systems with Applications: An International Journal
Empirical methods to determine the number of sources in single-channel musical signals
IEEE Transactions on Audio, Speech, and Language Processing
A new kalman filtering algorithm for nonlinear principal component analysis
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
An novel algorithm for blind source separation with unknown sources number
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Adaptive weighted orthogonal constrained algorithm for blind source separation
Digital Signal Processing
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The blind source separation (BSS) problem with an unknown number of sources is an important practical issue that is usually skipped by assuming that the source number n is known and equal to the number m of sensors. This letter studies the general BSS problem satisfying m ≥ n. First, it is shown that the mutual information of outputs of the separation network is a cost function for BSS, provided that the mixing matrix is of full column rank and the m × m separating matrix is nonsingular. The mutual information reaches its local minima at the separation points, where the m outputs consist of n desired source signals and m-n redundant signals. Second, it is proved that the natural gradient algorithm proposed primarily for complete BSS (m=n) can be generalized to deal with the overdetermined BSS problem (mn), but it would diverge inevitably due to lack of a stationary point. To overcome this shortcoming, we present a modified algorithm, which can perform BSS steadily and provide the desired source signals at specified channels if some matrix is designed properly. Finally, the validity of the proposed algorithm is confirmed by computer simulations on artificially synthesized data.