Source separation in structured nonlinear models

Author

Taleb, Anisse

Author_Institution

Sch. of Electr. & Comput. Eng., Curtin Univ. of Technol., Perth, WA, Australia

Volume

6

fYear

2001

fDate

2001

Firstpage

3513

Abstract

This paper discusses several issues related to blind source separation in nonlinear models. Specifically, separability results show that separation in the general case is impossible, however, for specific nonlinear models the problem does have a solution. A specific set of parametric nonlinear mixtures is considered; this set has the Lie group structure. In the parameter set, a group operation is defined and a relative gradient is defined. The latter is applied to design stochastic algorithms for which the equivariance property is shown

Keywords

Lie groups; adaptive filters; adaptive signal processing; filtering theory; gradient methods; nonlinear filters; parameter estimation; stochastic processes; Lie group structure; blind source separation; equivariance property; parametric nonlinear mixtures; relative gradient; separability; stochastic algorithms; structured nonlinear models; Algorithm design and analysis; Australia; Blind source separation; Nonlinear distortion; Source separation; Stochastic processes; Telecommunication computing;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP '01). 2001 IEEE International Conference on

Conference_Location

Salt Lake City, UT

ISSN

1520-6149

Print_ISBN

0-7803-7041-4

Type

conf

DOI

10.1109/ICASSP.2001.940599

Filename

940599