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Normalised natural gradient algorithm for the separation of cyclostationary sources

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conference contribution
posted on 2010-01-07, 16:12 authored by Maria G. Jafari, Jonathon Chambers
A normalised natural gradient algorithm (NGA) for the separation of cyclostationary source signals is proposed in this paper. It improves the convergence properties of the cyclostationary natural gradient algorithm (CSNGA) by employing a gradient adaptive learning rate whose value changes in response to some change in the filter parameters. Experimental results demonstrate the improved behaviour of the approach.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Citation

JAFARI, M.G. and CHAMBERS, J., 2003. Normalised natural gradient algorithm for the separation of cyclostationary sources. IN: IEEE International Conference on Acoustics, Speech, and Signal Processing, Volume 5, pp. V - 301-304

Publisher

© IEEE

Version

  • VoR (Version of Record)

Publication date

2003

Notes

This is a conference paper [© IEEE]. It is also available from: http://ieeexplore.ieee.org/servlet/opac?punumber=8535. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

ISBN

0780376633

ISSN

1520-6149

Language

  • en

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    Loughborough Publications

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    Keywords

    Adaptive filtersAdaptive signal processingConvergence of numerical methodsGradient methodsLearning (artificial intelligence)Source separationMechanical Engineering not elsewhere classified

    Licence

    CC BY-NC-ND 4.0

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