Analysis of ICA techniques in terms of Failure percentage and Average CPU Time for Real World BSS Task
Citation
Naveen Dubey, Rajesh Mehra"Analysis of ICA techniques in terms of Failure percentage and Average CPU Time for Real World BSS Task", International Journal of Engineering Trends and Technology (IJETT), V30(6),276-280 December 2015. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Abstract
Taking assumption the hidden sources
are statistically independent, Independent
component analysis technique separates these
sources from a linear mixture of audio signals,
communication signals generated by equally spaced
independent audio sources. Since, in Audio
applications source exhibit non - dependence.
Mutual information minimization corresponds to
minimization of entropy, that ensures quality of
separation and exploits non-Gaussianity, noncircularity
and sample dependence simultaneously.
In this paper these properties are exploited with the
help of Cramer-Rao lower bound, modified convex
divergence based ICA, Fast ICA and JADE. The
performance of these techniques are examined with
the help of a number of example and a comparative
analysis presented in term of failure percentage and
average CPU time taken for execution.
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Keywords
Independent Component Analysis,
Blind Source Separation, Convex Divergence,
Independence, non-Gaussianity.