Dimensional reduction using Blind source separation for identifying sources

Naik, GR; Kumar, DK

Dimensional reduction using Blind source separation for identifying sources

Naik, GR

Kumar, DK

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Publication Type:: Journal Article
Citation:: International Journal of Innovative Computing, Information and Control, 2011, 7 (2), pp. 989 - 1000
Issue Date:: 2011-02-01

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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	2011-02-01	en_US
dc.identifier.citation	International Journal of Innovative Computing, Information and Control, 2011, 7 (2), pp. 989 - 1000	en_US
dc.identifier.issn	1349-4198	en_US
dc.identifier.uri	http://hdl.handle.net/10453/27573
dc.description.abstract	Separation of independent sources using Blind Source Separation (BSS) techniques requires prior knowledge of the number of independent sources. Performing BSS when the number of recordings is greater than the number of sources can give erroneous results. Techniques employed to estimate suitable recordings from all the recordings require estimation of number of sources or require repeated iterations. This paper demonstrates that normalised determinant of the global matrix is a measure of the number of independent sources, K, in a mixture of M recordings. This paper also shows that performing ICA on K out of M randomly selected recordings gives good quality of separation. The qualities of the outcome of this experiment were measured using Signal to Interference Ratio (SIR) and Signal to Noise Ratio (SNR). The results demonstrate that using this technique, there is an improvement in the quality of separation as measured using SIR and SNRs. ICIC International © 2011.	en_US
dc.relation.ispartof	International Journal of Innovative Computing, Information and Control	en_US
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	Dimensional reduction using Blind source separation for identifying sources	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	7	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 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.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	7	en_US

Abstract:

Separation of independent sources using Blind Source Separation (BSS) techniques requires prior knowledge of the number of independent sources. Performing BSS when the number of recordings is greater than the number of sources can give erroneous results. Techniques employed to estimate suitable recordings from all the recordings require estimation of number of sources or require repeated iterations. This paper demonstrates that normalised determinant of the global matrix is a measure of the number of independent sources, K, in a mixture of M recordings. This paper also shows that performing ICA on K out of M randomly selected recordings gives good quality of separation. The qualities of the outcome of this experiment were measured using Signal to Interference Ratio (SIR) and Signal to Noise Ratio (SNR). The results demonstrate that using this technique, there is an improvement in the quality of separation as measured using SIR and SNRs. ICIC International © 2011.

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http://hdl.handle.net/10453/27573