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
Extraction of Specific Signals with Temporal Structure
Neural Computation
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
An algorithm for extracting fetal electrocardiogram
Neurocomputing
Bayesian nonstationary source separation
Neurocomputing
Sequential Blind Signal Extraction with the Linear Predictor
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Journal of Computational and Applied Mathematics
Linear prediction based blind source extraction algorithms in practical applications
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Extraction of signals with specific temporal structure using kernel methods
IEEE Transactions on Signal Processing
Noisy component extraction (NoiCE)
IEEE Transactions on Circuits and Systems Part I: Regular Papers
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
A flexible algorithm for extracting periodic signals
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
A two-stage based approach for extracting periodic signals
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Harmonic retrieval by period blind source extraction method: Model and algorithm
Digital Signal Processing
Hi-index | 0.02 |
There is a trend to develop blind or semi-blind source extraction algorithms based on second-order statistics, due to its low computation load and fast processing speed. An important and primary work is done by Barros and Cichocki, who propose an extraction algorithm based on a time delay. The algorithm is simple and fast, but its performance is not satisfying. The paper extends their work and proposes a robust algorithm based on eigenvalue decomposition of several delayed covariance matrices. It is faster and has better performance, which is confirmed by theoretical analysis and computer simulations.