Name
Abstract | ||
|---|---|---|
In this paper we develop a new method for blind separation of temporally correlated sources, possibly dependent signals from linear mixtures of them. The proposed algorithm is based on the mutual independency of the innovations of source signals instead of original signals. This algorithm takes into account both the temporal structure and the high-order statistics of source signals and in contrast to the most known blind separation algorithms only exploiting the second order statistics or the non-Gaussianity. In this framework, a fixed-point algorithm is introduced. The fixed-point algorithm is computationally very simple, converge fast, and does not need choose any learning step sizes. Extensive computer simulations with speech signals and images confirm the validity and high performance of the proposed algorithm. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/ICME.2007.4284626 | 2007 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, VOLS 1-5 |
Keywords | Field | DocType |
covariance matrix,blind source separation,image processing,computer simulation,independent component analysis,automation,statistics,speech processing,fixed point,statistical analysis | Speech processing,Pattern recognition,Computer science,Fixed point algorithm,Image processing,Artificial intelligence,Blind signal separation,Statistical analysis | Conference |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhenwei Shi | 1 | 559 | 63.11 |
| Dan Zhang | 2 | 461 | 22.17 |
| Changshui Zhang | 3 | 5506 | 323.40 |