Exploiting nonlinearity in adaptive signal processing

Phebe Vayanos*, Mo Chen, Beth Jelfs, Danilo P. Mandic

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Quantitative performance criteria for the analysis of machine learning architectures and algorithms have been long established. However, the qualitative performance criteria, e.g., nonlinearity assessment, are still emerging. To that end, we employ some recent developments in signal characterisation and derive criteria for the assessment of the changes in the nature of the processed signal. In addition, we also propose a novel online method for tracking the system nonlinearity. A comprehensive set of simulations in both the linear and nonlinear settings and their combination supports the analysis.

Original languageEnglish
Title of host publicationAdvances in Nonlinear Speech Processing - International Conference on Nonlinear Speech Processing, NOLISP 2007, Revised Selected Papers
PublisherSpringer Verlag
Number of pages21
ISBN (Print)3540773460, 9783540773467
Publication statusPublished - 2007
Externally publishedYes
EventInternational Conference on Nonlinear Speech Processing, NOLISP 2007 - Paris, France
Duration: 22 May 200725 May 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4885 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Nonlinear Speech Processing, NOLISP 2007

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


Dive into the research topics of 'Exploiting nonlinearity in adaptive signal processing'. Together they form a unique fingerprint.

Cite this