Probabilistic geometric approach to blind separation of time-varying mixtures

Authors:
Ran Kaftory;Yehoshua Y. Zeevi
Affiliations:
Technion-Israel Institute of Technology, Department of Electrical Engineering, Haifa, Israel;Technion-Israel Institute of Technology, Department of Electrical Engineering, Haifa, Israel
Venue:
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Year:
2007

Citing 3
Cited 3

Blind Source Separation by Sparse Decomposition in a Signal Dictionary

Neural Computation
Adaptive Algorithm for Blind Separation from Noisy Time-Varying Mixtures

Neural Computation
Separation of periodically time-varying mixtures using second-order statistics

ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation

Blind separation of position varying mixed images

ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Independent component analysis of time/position varying mixtures

LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Sparse source separation of non-instantaneous spatially varying single path mixtures

ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV

Quantified Score

Hi-index	0.00

Visualization

Abstract

We consider the problem of blindly separating time-varying instantaneous mixtures. It is assumed that the arbitrary time dependency of the mixing coefficient, is known up to a finite number of parameters. Using sparse (or sparsified) sources, we geometrically identify samples of the curves representing the parametric model. The parameters are found using a probabilistic approach of estimating the maximum likelihood of a curve, given the data. After identifying the model parameters, the mixing system is inverted to estimate the sources. The new approach to blind separation of time-varying mixtures is demonstrated using both synthetic and real data.