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A modified underdetermined blind source separation algorithm using competitive learning

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conference contribution
posted on 2010-02-04, 17:27 authored by Yuhui Luo, Jonathon Chambers
The problem of underdetermined blind source separation is addressed. An advanced classification method based upon competitive learning is proposed for automatically determining the number of active sources over the observation. Its introduction in underdetermined blind source separation successfully overcomes the drawback of an existing method, in which the goal of separating more sources than the number of available mixtures is achieved by exploiting the sparsity of the non-stationary sources in the time-frequency domain. Simulation studies are presented to support the proposed approach.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Citation

LUO, Y. and CHAMBERS, J.A., 2003. A modified underdetermined blind source separation algorithm using competitive learning. IN: Proceedings of 2003 3rd International Symposium on Image and Signal Processing and Analysis (ISPA 2003), Rome, Italy, 18-20 September, Vol. 2, pp. 966-969.

Publisher

© IEEE

Version

  • VoR (Version of Record)

Publication date

2003

Notes

This is a conference paper [© IEEE]. It is also available at: 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

953-184-061-X

ISSN

1330-1012

Language

  • en

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