Abstract
It is a known fact that blind algorithms have convergence times of an order of magnitude longer than their nonblind counterparts. However, as shown in this letter, the knowledge of a subset of signals can greatly accelerate the convergence of blind source separation. The convergence behavior of the proposed algorithm is compared with the blind-only case.
| Original language | English |
|---|---|
| Pages (from-to) | 236-238 |
| Number of pages | 3 |
| Journal | IEEE Signal Processing Letters |
| Volume | 8 |
| Issue number | 8 |
| DOIs | |
| State | Published - Aug 2001 |
| Externally published | Yes |
Bibliographical note
Funding Information:Manuscript received February 28, 2000. This work was supported in part by the Swiss Federal Institute of Technology, ETH Zurich, Switzerland. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. H. Messer-Yaron.
Funding
Manuscript received February 28, 2000. This work was supported in part by the Swiss Federal Institute of Technology, ETH Zurich, Switzerland. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. H. Messer-Yaron.
| Funders | Funder number |
|---|---|
| Swiss Federal Institute of Technology | |
| Eidgenössische Technische Hochschule Zürich |
Keywords
- Adaptive blind source separation
- Blind signal processing
- Natural gradient learning algorithm
- Semi-blind learning algorithm
- Teleconferencing
- Virtual sensors