2134/5530
Yuhui Luo
Yuhui
Luo
Wenwu Wang
Wenwu
Wang
Jonathon Chambers
Jonathon
Chambers
Sangarapillai Lambotharan
Sangarapillai
Lambotharan
Ian Proudler
Ian
Proudler
Exploitation of source nonstationarity in underdetermined blind source separation with advanced clustering techniques
Loughborough University
2009
Gap statistics
Self-splitting competitive learning (SSCL)
Time-frequency (TF) representation
Underdetermined blind source separation (BSS)
Mechanical Engineering not elsewhere classified
2009-11-26 14:37:09
Journal contribution
https://repository.lboro.ac.uk/articles/journal_contribution/Exploitation_of_source_nonstationarity_in_underdetermined_blind_source_separation_with_advanced_clustering_techniques/9560312
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.