Exploiting nonlinearity in adaptive signal processing

Phebe Vayanos; Mo Chen; Beth Jelfs; Danilo P. Mandic

doi:10.1007/978-3-540-77347-4_3

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 proceeding › Conference contribution

Abstract

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 language	English
Title of host publication	Advances in Nonlinear Speech Processing - International Conference on Nonlinear Speech Processing, NOLISP 2007, Revised Selected Papers
Publisher	Springer Verlag
Pages	57-77
Number of pages	21
ISBN (Print)	3540773460, 9783540773467
DOIs	https://doi.org/10.1007/978-3-540-77347-4_3
Publication status	Published - 2007
Externally published	Yes
Event	International Conference on Nonlinear Speech Processing, NOLISP 2007 - Paris, France Duration: 22 May 2007 → 25 May 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4885 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Nonlinear Speech Processing, NOLISP 2007
Country/Territory	France
City	Paris
Period	22/05/07 → 25/05/07

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-540-77347-4_3

Cite this

Vayanos, P., Chen, M., Jelfs, B., & Mandic, D. P. (2007). Exploiting nonlinearity in adaptive signal processing. In Advances in Nonlinear Speech Processing - International Conference on Nonlinear Speech Processing, NOLISP 2007, Revised Selected Papers (pp. 57-77). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4885 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-540-77347-4_3

Vayanos, Phebe ; Chen, Mo ; Jelfs, Beth et al. / Exploiting nonlinearity in adaptive signal processing. Advances in Nonlinear Speech Processing - International Conference on Nonlinear Speech Processing, NOLISP 2007, Revised Selected Papers. Springer Verlag, 2007. pp. 57-77 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Exploiting nonlinearity in adaptive signal processing",

abstract = "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.",

author = "Phebe Vayanos and Mo Chen and Beth Jelfs and Mandic, {Danilo P.}",

year = "2007",

doi = "10.1007/978-3-540-77347-4_3",

language = "English",

isbn = "3540773460",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

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booktitle = "Advances in Nonlinear Speech Processing - International Conference on Nonlinear Speech Processing, NOLISP 2007, Revised Selected Papers",

note = "International Conference on Nonlinear Speech Processing, NOLISP 2007 ; Conference date: 22-05-2007 Through 25-05-2007",

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Vayanos, P, Chen, M, Jelfs, B & Mandic, DP 2007, Exploiting nonlinearity in adaptive signal processing. in Advances in Nonlinear Speech Processing - International Conference on Nonlinear Speech Processing, NOLISP 2007, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4885 LNAI, Springer Verlag, pp. 57-77, International Conference on Nonlinear Speech Processing, NOLISP 2007, Paris, France, 22/05/07. https://doi.org/10.1007/978-3-540-77347-4_3

Exploiting nonlinearity in adaptive signal processing. / Vayanos, Phebe; Chen, Mo; Jelfs, Beth et al.

Advances in Nonlinear Speech Processing - International Conference on Nonlinear Speech Processing, NOLISP 2007, Revised Selected Papers. Springer Verlag, 2007. p. 57-77 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4885 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

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AU - Jelfs, Beth

AU - Mandic, Danilo P.

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N2 - 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.

AB - 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.

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SN - 9783540773467

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Vayanos P, Chen M, Jelfs B, Mandic DP. Exploiting nonlinearity in adaptive signal processing. In Advances in Nonlinear Speech Processing - International Conference on Nonlinear Speech Processing, NOLISP 2007, Revised Selected Papers. Springer Verlag. 2007. p. 57-77. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-77347-4_3

Exploiting nonlinearity in adaptive signal processing

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ASJC Scopus subject areas

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