Matrix analysis
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
A neural net for blind separation of nonstationary signals
Neural Networks
A fast fixed-point algorithm for independent component analysis
Neural Computation
Natural gradient works efficiently in learning
Neural Computation
Equivariant nonstationary source separation
Neural Networks
Adaptive separation of independent sources: a deflation approach
ICASSP '94 Proceedings of the Acoustics, Speech, and Signal Processing,1994. on IEEE International Conference - Volume 04
Globally convergent blind source separation based on a multiuser kurtosis maximization criterion
IEEE Transactions on Signal Processing
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
A matrix-pencil approach to blind separation of colorednonstationary signals
IEEE Transactions on Signal Processing
Blind separation of independent sources for virtually any sourceprobability density function
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Independent component analysis and (simultaneous) third-ordertensor diagonalization
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Serial updating rule for blind separation derived from the methodof scoring
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 generalization of joint-diagonalization criteria for sourceseparation
IEEE Transactions on Signal Processing
Cumulant-based independence measures for linear mixtures
IEEE Transactions on Information Theory
Efficient source adaptivity in independent component analysis
IEEE Transactions on Neural Networks
Blind source separation by nonstationarity of variance: a cumulant-based approach
IEEE Transactions on Neural Networks
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This paper presents new results on blind separation of instantaneously mixed independent sources based on high-order statistics together with their time and frequency nonproperties (i.e., the non-stationarity and non-whiteness of sources). Separation criteria of mixtures are established on a set of cumulants at different time instants using the non-stationarity of sources and/or time-delayed cumulants using the non-whiteness of sources. It is shown that cumulants at different time instants and time-delayed cumulants can be used as criteria for blind source separation (BSS). Furthermore, it is proved that the cumulant-based separation criteria are directly related to the separability conditions. Batch-data and online learning rules are developed based on the joint diagonalization of symmetric fourth-order cumulant matrices, and the learning rules are further simplified to correlation-based BSS algorithms. In addition, an initialization strategy is proposed for improving the convergence of the learning rules. Simulation results are given to demonstrate the validity and performance of the algorithms.