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Blind separation of convolutive mixtures of cyclostationary sources using an extended natural gradient method

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conference contribution
posted on 05.02.2010, 15:17 authored by Wenwu Wang, Maria G. Jafari, Saeid Sanei, Jonathon Chambers
An on-line adaptive blind source separation algorithm for the separation of convolutive mixtures of cyclostationary source signals is proposed. The algorithm is derived by applying natural gradient iterative learning to the novel cost function which is defined according to the wide sense cyclostationarity of signals. The efficiency of the algorithm is supported by simulations, which show that the proposed algorithm has improved performance for the separation of convolved cyclostationary signals in terms of convergence speed and waveform similarity measurement, as compared to the conventional natural gradient algorithm for convolutive mixtures.

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School

  • Mechanical, Electrical and Manufacturing Engineering

Citation

WANG, W. ... et al., 2003. Blind separation of convolutive mixtures of cyclostationary sources using an extended natural gradient method. IN: Proceedings of 2003 7th International Symposium on Signal Processing and Its Applications (ISSPA 2003), Paris, France, 1-4 July, Vol. 2, pp. 93-96.

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© IEEE

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VoR (Version of Record)

Publication date

2003

Notes

This is a conference paper [© IEEE]. It is also available from: http://ieeexplore.ieee.org/. 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

0-7803-7946-2

Language

en

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