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
Jacobi Angles for Simultaneous Diagonalization
SIAM Journal on Matrix Analysis and Applications
Second Order Nonstationary Source Separation
Journal of VLSI Signal Processing Systems
Equivariant nonstationary source separation
Neural Networks
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Blind source separation via generalized eigenvalue decomposition
The Journal of Machine Learning Research
A unifying model for blind separation of independent sources
Signal Processing
Fast approximate joint diagonalization incorporating weight matrices
IEEE Transactions on Signal Processing
Speed and accuracy enhancement of linear ICA techniques using rational nonlinear functions
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
Blind separation of mixture of independent sources through aquasi-maximum likelihood approach
IEEE Transactions on Signal Processing
Blind separation of instantaneous mixtures of nonstationary sources
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Performance measurement in blind audio source separation
IEEE Transactions on Audio, Speech, and Language Processing
Spatio–Temporal FastICA Algorithms for the Blind Separation of Convolutive Mixtures
IEEE Transactions on Audio, Speech, and Language Processing
Maximum likelihood and lower bounds in system identification with non-Gaussian inputs
IEEE Transactions on Information Theory
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Independent component analysis based on nonparametric density estimation
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Random pruning of blockwise stationary mixtures for online BSS
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
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We address independent component analysis (ICA) of piecewise stationary and non-Gaussian signals and propose a novel ICA algorithm called Block EFICA that is based on this generalized model of signals. The method is a further extension of the popular non-Gaussianity-based FastICA algorithm and of its recently optimized variant called EFICA. In contrast to these methods, Block EFICA is developed to effectively exploit varying distribution of signals, thus, also their varying variance in time (nonstationarity) or, more precisely, in time-intervals (piecewise stationarity). In theory, the accuracy of the method asymptotically approaches Cramer-Rao lower bound (CRLB) under common assumptions when variance of the signals is constant. On the other hand, the performance is practically close to the CRLB even when variance of the signals is changing. This is demonstrated by comparing our algorithm with various methods that are asymptotically efficient within ICA models based either on the non-Gaussianity or the nonstationarity. The benefit of our algorithm is demonstrated by examples with real-world audio signals.