Name
Abstract | ||
|---|---|---|
In this paper we propose and investigate a recursive approach for blind source separations (BSS) or/and for independent component analyses (ICA). Based on this approach we present a deterministic (without a stochastic learning) algorithm for real-time blind source separation of convolutive mixing. When employed to acoustic signals, the algorithm shows a superior rate of convergence over its counterpart of gradient-based approach based on our simulations. By applying the algorithm in a real-time BSS system for realistic acoustic signals, we also give experiments to illustrate the effectiveness and validity of the algorithm. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1007/978-3-540-45224-9_196 | LECTURE NOTES IN COMPUTER SCIENCE |
Keywords | Field | DocType |
real time,blind source separation,rate of convergence | Computer science,Algorithm,Speech recognition,Independent component analysis,Rate of convergence,Blind signal separation,Recursion | Conference |
Volume | ISSN | Citations |
2773 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 3 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuxue Ding | 1 | 235 | 33.84 |
| Jie Huang | 2 | 33 | 4.47 |