University of Surrey

Test tubes in the lab Research in the ATI Dance Research

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

Luo, Y, Wang, W, Chambers, JA, Lambotharan, S and Proudler, I (2006) Exploitation of source nonstationarity in underdetermined blind source separation with advanced clustering techniques IEEE Transactions on Signal Processing, 54 (6 I). pp. 2198-2212.

[img] Text
Restricted to Repository staff only
Available under License : See the attached licence file.

Download (930kB)
[img] Text (licence)
Restricted to Repository staff only

Download (33kB)


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. © 2006 IEEE.

Item Type: Article
Authors :
Luo, Y
Wang, W
Chambers, JA
Lambotharan, S
Proudler, I
Date : June 2006
DOI : 10.1109/TSP.2006.873367
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 14:43
Last Modified : 31 Oct 2017 14:37

Actions (login required)

View Item View Item


Downloads per month over past year

Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800