Name
Abstract | ||
|---|---|---|
We consider the problem of joint blind source separation of multiple datasets and introduce an effective solution to the problem. We pose the problem in an independent vector analysis (IVA) framework utilizing the multivariate Gaussian source vector distribution. We provide a new general IVA implementation using a decoupled nonorthogonal optimization algorithm and establish the connection between the new approach and another approach using second-order statistics, multiset canonical correlation analysis. Experimental results are given to demonstrate the success of the new algorithm in achieving reliable source separation for both Gaussian and non-Gaussian sources. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-15995-4_44 | LVA/ICA |
Keywords | Field | DocType |
multivariate gaussian source vector,new general iva implementation,non-gaussian source,decoupled nonorthogonal optimization algorithm,reliable source separation,multiset canonical correlation analysis,independent vector analysis,nonorthogonal independent vector analysis,multivariate gaussian model,new algorithm,new approach,joint blind source separation,blind source separation,canonical correlation analysis | Mathematical optimization,Multiset,Canonical correlation,Algorithm,Gaussian,Multivariate normal distribution,Independent component analysis,Independent vector analysis,Blind signal separation,Source separation,Mathematics | Conference |
Volume | ISSN | ISBN |
6365 | 0302-9743 | 3-642-15994-X |
Citations | PageRank | References |
15 | 0.87 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthew Anderson | 1 | 263 | 14.64 |
| Xi-Lin Li | 2 | 547 | 34.85 |
| Tülay Adali | 3 | 1690 | 126.40 |