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Title: Exploitation of source nonstationarity in underdetermined blind source separation with advanced clustering techniques
Authors: Luo, Yuhui
Wang, Wenwu
Chambers, Jonathon
Lambotharan, Sangarapillai
Proudler, Ian
Keywords: Gap statistics
Self-splitting competitive learning (SSCL)
Time-frequency (TF) representation
Underdetermined blind source separation (BSS)
Issue Date: 2006
Publisher: © IEEE
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
Abstract: 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.
Description: 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.
Version: Published
DOI: 10.1109/TSP.2006.873367
URI: https://dspace.lboro.ac.uk/2134/5530
ISSN: 1053-587X
Appears in Collections:Published Articles (Mechanical, Electrical and Manufacturing Engineering)

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