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)||
|© 2015 by IJETT Journal|
|Year of Publication : 2015|
|Authors : Naveen Dubey, Rajesh Mehra
|DOI : 10.14445/22315381/IJETT-V30P252|
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
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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Independent Component Analysis, Blind Source Separation, Convex Divergence, Independence, non-Gaussianity.