Name
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-74494-8_47 | ICA |
Keywords | Field | DocType |
finite number,time-varying instantaneous mixture,parametric model,time-varying mixture,model parameter,arbitrary time dependency,new approach,probabilistic geometric approach,blind separation,probabilistic approach,maximum likelihood | Applied mathematics,Mathematical optimization,Finite set,Parametric model,Maximum likelihood,Independent component analysis,Probabilistic logic,Blind signal separation,Mathematics | Conference |
Volume | ISSN | ISBN |
4666 | 0302-9743 | 3-540-74493-2 |
Citations | PageRank | References |
4 | 0.51 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ran Kaftory | 1 | 27 | 2.99 |
| Yehoshua Y. Zeevi | 2 | 610 | 248.69 |