Matrix analysis
A neural net for blind separation of nonstationary signals
Neural Networks
Second Order Nonstationary Source Separation
Journal of VLSI Signal Processing Systems
Equivariant nonstationary source separation
Neural Networks
Second-order blind source separation in the Fourier space of data
Signal Processing
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Second-order blind separation of sources based on canonical partialinnovations
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Blind source-separation using second-order cyclostationarystatistics
IEEE Transactions on Signal Processing
Blind separation of instantaneous mixtures of nonstationary sources
IEEE Transactions on Signal Processing
Blind source separation based on time-frequency signalrepresentations
IEEE Transactions on Signal Processing
Complex random vectors and ICA models: identifiability, uniqueness, and separability
IEEE Transactions on Information Theory
Blind source separation by nonstationarity of variance: a cumulant-based approach
IEEE Transactions on Neural Networks
Blind Separation of Noisy Mixtures of Non-stationary Sources Using Spectral Decorrelation
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
ICISP'12 Proceedings of the 5th international conference on Image and Signal Processing
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Blind source separation (BSS) methods aim at restoring source signals from their mixtures. For linear instantaneous mixtures of stationary random sources, a natural and widely used approach consists in using some statistics associated to the temporal representation of the signals. On the contrary, we here consider non-stationary real sources and we show that they have interesting frequency-domain properties which motivate the introduction of two new frequency-domain BSS methods. The first method works by diagonalizing a zero-lag, second-order statistics matrix, created using both covariance and pseudo-covariance matrices of Fourier transforms of real-valued observations. In practice, this method is specially suitable for separating cyclo-stationary sources. The second method is particularly important because it allows the existing time-domain algorithms developed for stationary, temporally correlated sources (like AMUSE or SOBI) to be extended to non-stationary, temporally uncorrelated sources just by mapping the mixtures into the frequency domain. Both methods set no constraint on the piecewise stationarity of the sources, unlike most previously reported BSS methods exploiting source non-stationarity. The experimental results using artificial and real-world sources confirm the good performance of the proposed methods for non-stationary sources.