Name
Abstract | ||
|---|---|---|
This paper studies the blind source separation (BSS) problem with the assumption that the source signals are cyclostationary. Identifiability and separability criteria based on second-order cyclostationary statistics (SOCS) alone are derived. The identifiability condition is used to define an appropriate contrast function. An iterative algorithm (ATH2) is derived to minimize this contrast function. This algorithm separates the sources even when they do not have distinct cycle frequencies |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1109/78.912913 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
separability criteria,identifiability criteria,cyclostationary source signals,signal processing,second-order statistics,separation criteria,identification,identifiability condition,statistical analysis,iterative method,higher order statistics,cyclostationary source,blind source separation,iterative algorithm,gradient methods,gradient method,second-order cyclostationary statistics,blind source-separation,signal detection,iterative methods,contrast function minimisation,frequency,engineering,remote sensing,speech processing,statistics,second order | Signal processing,Speech processing,Iterative method,Identifiability,Divergence (statistics),Statistics,Blind signal separation,Source separation,Mathematics,Cyclostationary process | Journal |
Volume | Issue | ISSN |
49 | 4 | 1053-587X |
ISBN | Citations | PageRank |
0-7803-5256-4 | 55 | 2.67 |
References | Authors | |
11 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| K. Abed-Meraim | 1 | 1962 | 164.99 |
| Yong Xiang | 2 | 84 | 7.18 |
| Jonathan Manton | 3 | 167 | 20.60 |
| Y. Hua | 4 | 209 | 19.58 |