Architectures and algorithms for nonlinear adaptive filters

Author

Hegde, V. ; Radhakrishnan, C. ; Krusienski, D.J. ; Jenkins, W.K.

Author_Institution

Dept. of Electr. Eng., Pennsylvania State Univ., University Park, PA, USA

Volume

2

fYear

2002

fDate

3-6 Nov. 2002

Firstpage

1015

Abstract

This paper considers series-cascade nonlinear adaptive filter architectures consisting of a linear input filter, a memoryless polynomial nonlinearity, and a linear output filter (LNL). The learning characteristics of the LNL structure are studied in terms of performance and complexity. Replacing the linear input stage and the memoryless nonlinear stage of the LNL model with a Volterra module is then considered. Adaptive algorithms are summarized for these structures and experimental examples are used to illustrate performance for the identification of an acoustic echo channel.

Keywords

FIR filters; acoustic signal processing; adaptive filters; cascade networks; channel estimation; computational complexity; convergence of numerical methods; echo; identification; nonlinear filters; FIR series-cascade structure; LNL structure; Volterra module; acoustic echo channel identification; adaptive algorithms; computational complexity; convergence; learning characteristics; linear input filter; linear output filter; loudspeaker; memoryless nonlinear stage; memoryless polynomial nonlinearity; nonlinear adaptive filter algorithms; nonlinear adaptive filter architectures; performance; series-cascade adaptive filter; Acoustic noise; Adaptive filters; Echo cancellers; Finite impulse response filter; IIR filters; Loudspeakers; Nonlinear filters; Poles and zeros; Polynomials; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems and Computers, 2002. Conference Record of the Thirty-Sixth Asilomar Conference on

Conference_Location

Pacific Grove, CA, USA

ISSN

1058-6393

Print_ISBN

0-7803-7576-9

Type

conf

DOI

10.1109/ACSSC.2002.1196937

Filename

1196937