JOURNAL OF COMPUTERS (JCP)
ISSN : 1796-203X
Volume : 2    Issue : 6    Date : August 2007

Exploiting Sparsity, Sparseness and Super-Gaussianity in Underdetermined Blind Identification
of Temporomandibular Joint Sounds
Clive Cheong Took and Saeid Sanei
Page(s): 65-71
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Abstract
In this paper, we study a 2 × 3 temporomandibular joint (TMJ) underdetermined blind source
separation (UBSS). This particular UBSS has been subject to an empirical experiment performed
previously on two sparse TMJ sources and a non-sparse source modelled as super-Gaussian
noise. In this study, we found that FastICA algorithm tends to separate the two highly super -
Gaussian sources when applied to the mixtures. When these two mixtures were filtered, FastICA
focused on the non-sparse source (i.e. noise). Previously, we did not examine why such filtering
approach would lead to estimation of the nonsparse source. To this end, the objective is to provide
an extensive set of simulations to demonstrate why this filtering approach fully solve this particular
underdetermined blind identification. We have employed the shape parameter α of the generalized
Gaussian distribution (GGD) as a measure of sparseness and Gaussianity. This parameter was
also utilized to illustrate the convergence of our filtering approach and the sub-Gaussian effect of the
filter on the mixtures. Moreover, we have also considered the case where the noise source is
modelled as sub-Gaussian and Gaussian as an extension of our previous work. Simulation studies
show that our filtering approach is robust and performs well in this particular TMJ UBSS application.

Index Terms
sparsity, sparseness, Gaussianity, moving average filter, independent component analysis