Jornal article

Koldovský Z., Tichavský P.

Lecture Notes in Computer Science

2007

730-737

0302-9743

Ing. Petr Tichavský, CSc. (ÚTIA - Oddělení stochastické informatiky)

blind source separation, independent component analysis

In this paper, a variant of the well known algorithm FastICA isproposed to be used for blind source separation in off-line (block processing) setup and a noisy environment. The algorithm combines a symmetric FastICA with test of saddle points to achieve fast global convergence and a one-unit refinement to obtain high noise rejection ability. A novel test of saddle points is designed for separation of complex-valued signals. The bias of the proposed algorithm due to additive noise can be shown to be asymptotically proportional to $/sigma^3$ for small $/sigma$, where $/sigma^2$ is the variance of the additive noise.