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Exploitation of source nonstationarity in underdetermined blind source separation with advanced clustering techniques

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posted on 26.11.2009 by Yuhui Luo, Wenwu Wang, Jonathon Chambers, Sangarapillai Lambotharan, Ian Proudler
The problem of blind source separation (BSS) is investigated. Following the assumption that the time-frequency (TF) distributions of the input sources do not overlap, quadratic TF representation is used to exploit the sparsity of the statistically nonstationary sources. However, separation performance is shown to be limited by the selection of a certain threshold in classifying the eigenvectors of the TF matrices drawn from the observation mixtures. Two methods are, therefore, proposed based on recently introduced advanced clustering techniques, namely Gap statistics and self-splitting competitive learning (SSCL), to mitigate the problem of eigenvector classification. The novel integration of these two approaches successfully overcomes the problem of artificial sources induced by insufficient knowledge of the threshold and enables automatic determination of the number of active sources over the observation. The separation performance is thereby greatly improved. Practical consequences of violating the TF orthogonality assumption in the current approach are also studied, which motivates the proposal of a new solution robust to violation of orthogonality. In this new method, the TF plane is partitioned into appropriate blocks and source separation is thereby carried out in a block-by-block manner. Numerical experiments with linear chirp signals and Gaussian minimum shift keying (GMSK) signals are included which support the improved performance of the proposed approaches.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Citation

LUO, Y. ... et al, 2006. Exploitation of source nonstationarity in underdetermined blind source separation with advanced clustering techniques. IEEE Transactions on Signal Processing, 54 (6), pt.1, pp. 2198-2212

Publisher

© IEEE

Version

VoR (Version of Record)

Publication date

2006

Notes

This article was published in the journal IEEE Transactions on Signal Processing [© IEEE] and 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.

ISSN

1053-587X

Language

en

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