Investigating the performance analysis of EASI algorithm and EKENS algorithm in nonlinear model

Author

Leong, W.Y. ; Homer, J.

Author_Institution

Sch. of Inf. Technol. & Electr. Eng., Queensland Univ., St. Lucia, Qld., Australia

Volume

1

fYear

2004

fDate

26-29 Oct. 2004

Firstpage

415

Abstract

This paper investigates the performance of EASI algorithm and the proposed EKENS algorithm for linear and nonlinear mixtures. The proposed EKENS algorithm is based on the modified equivariant algorithm and kernel density estimation. Theory and characteristic of both the algorithms are discussed for blind source separation model. The separation structure of nonlinear mixtures is based on a nonlinear stage followed by a linear stage. Simulations with artificial and natural data demonstrate the feasibility and good performance of the proposed EKENS algorithm.

Keywords

adaptive signal processing; blind source separation; independent component analysis; signal detection; EASI algorithm; EKENS algorithm; artificial data; blind source separation model; equivariant kernel nonlinear separation algorithm; kernel density estimation; linear mixtures; linear stage; modified equivariant algorithm; natural data; nonlinear mixtures; nonlinear model; nonlinear stage; performance analysis; separation structure; simulations; Biomedical signal processing; Blind source separation; Brain modeling; Image analysis; Independent component analysis; Kernel; Performance analysis; Signal processing algorithms; Source separation; Speech enhancement;

fLanguage

English

Publisher

ieee

Conference_Titel

Communications and Information Technology, 2004. ISCIT 2004. IEEE International Symposium on

Print_ISBN

0-7803-8593-4

Type

conf

DOI

10.1109/ISCIT.2004.1412879

Filename

1412879