2006 Special issue: Exploratory analysis of climate data using source separation methods
Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
A Maximum-Likelihood Interpretation for Slow Feature Analysis
Neural Computation
Temporally correlated source separation using variational Bayesian learning approach
Digital Signal Processing
Denoising Single Trial Event Related Magnetoencephalographic Recordings
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Complex blind source extraction from noisy mixtures using second-order statistics
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Frequency-based separation of climate signals
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Separation of nonlinear image mixtures by denoising source separation
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Comparison of BSS methods for the detection of α-activity components in EEG
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Blind source separation of cardiac murmurs from heart recordings
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Wavelet denoising as preprocessing stage to improve ICA performance in atrial fibrillation analysis
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Hi-index | 0.00 |
A new algorithmic framework called denoising source separation (DSS) is introduced. The main benefit of this framework is that it allows for the easy development of new source separation algorithms which can be optimised for specific problems. In this framework, source separation algorithms are constructed around denoising procedures. The resulting algorithms can range from almost blind to highly specialised source separation algorithms. Both simple linear and more complex nonlinear or adaptive denoising schemes are considered. Some existing independent component analysis algorithms are reinterpreted within the DSS framework and new, robust blind source separation algorithms are suggested. The framework is derived as a one-unit equivalent to an EM algorithm for source separation. However, in the DSS framework it is easy to utilise various kinds of denoising procedures which need not be based on generative models.In the experimental section, various DSS schemes are applied extensively to artificial data, to real magnetoencephalograms and to simulated CDMA mobile network signals. Finally, various extensions to the proposed DSS algorithms are considered. These include nonlinear observation mappings, hierarchical models and over-complete, nonorthogonal feature spaces. With these extensions, DSS appears to have relevance to many existing models of neural information processing.