.png){kind=icon}
.png){kind=icon}
Return
Two contributions to blind source separation using time-frequency distributions
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2003.819343.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We present two improvements/extensions of a previous deterministic blind source separation (BSS) technique, by Belouchrani and Amin, that involves joint-diagonalization of a set of Cohen's class spatial time-frequency distributions. The first contribution concerns the extension of the BSS technique to the stochastic case using Spatial Wigner-Ville spectrum. Then, we show that Belouchrani and Amin's technique can be interpreted as a practical implementation of the general equations we provide in the stochastic case. The second contribution is a new criterion aimed at selecting mole efficiently the time-frequency locations where the spatial matrices should be joint-diagonalized, introducing single autoterms selection. Simulation results on stochastic time-varying autoregressive moving average (TVARMA) signals demonstrate the improved efficiency of the method.
Keywords:
blind source separation (BSS)
nonstationary sources
spatial time-frequency distributions
spatial Wigner-Ville spectrum (SWVS)
Journal
IF:
9.6
Papers:
1.1W
Citations:
1.7W
Organization
No organization information available