Determining number of independent sources in undercomplete mixture

Naik, GR; Kumar, DK

Determining number of independent sources in undercomplete mixture

Naik, GR

Kumar, DK

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Publication Type:: Journal Article
Citation:: Eurasip Journal on Advances in Signal Processing, 2009, 2009
Issue Date:: 2009-12-09

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Field	Value	Language
dc.contributor.author	Naik, GR https://orcid.org/0000-0003-1790-9838	en_US
dc.contributor.author	Kumar, DK	en_US
dc.date.issued	2009-12-09	en_US
dc.identifier.citation	Eurasip Journal on Advances in Signal Processing, 2009, 2009	en_US
dc.identifier.issn	1687-6172	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26645
dc.description.abstract	Separation of independent sources using independent component analysis (ICA) requires prior knowledge of the number of independent sources. Performing ICA when the number of recordings is greater than the number of sources can give erroneous results. To improve the quality of separation, the most suitable recordings have to be identified before performing ICA. Techniques employed to estimate suitable recordings require estimation of number of independent sources or require repeated iterations. However there is no objective measure of the number of independent sources in a given mixture. Here, a technique has been developed to determine the number of independent sources in a given mixture. This paper demonstrates that normalised determinant of the global matrix is a measure of the number of independent sources, N, in a mixture of M recordings. It has also been shown that performing ICA on N randomly selected recordings out of M recordings gives good quality of separation. © 2009 G. R. Naik and D. K. Kumar.	en_US
dc.relation.ispartof	Eurasip Journal on Advances in Signal Processing	en_US
dc.relation.isbasedon	10.1155/2009/694850	en_US
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	Determining number of independent sources in undercomplete mixture	en_US
dc.type	Journal Article
utslib.citation.volume	2009	en_US
utslib.for	090609 Signal Processing	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	2009	en_US

Abstract:

Separation of independent sources using independent component analysis (ICA) requires prior knowledge of the number of independent sources. Performing ICA when the number of recordings is greater than the number of sources can give erroneous results. To improve the quality of separation, the most suitable recordings have to be identified before performing ICA. Techniques employed to estimate suitable recordings require estimation of number of independent sources or require repeated iterations. However there is no objective measure of the number of independent sources in a given mixture. Here, a technique has been developed to determine the number of independent sources in a given mixture. This paper demonstrates that normalised determinant of the global matrix is a measure of the number of independent sources, N, in a mixture of M recordings. It has also been shown that performing ICA on N randomly selected recordings out of M recordings gives good quality of separation. © 2009 G. R. Naik and D. K. Kumar.

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http://hdl.handle.net/10453/26645