Open Access

Determining Number of Independent Sources in Undercomplete Mixture

EURASIP Journal on Advances in Signal Processing20092009:694850

Received: 14 March 2009

Accepted: 2 September 2009

Published: 28 September 2009


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.


Information TechnologyPrior KnowledgeObjective MeasureQuantum InformationIndependent Component Analysis

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Authors’ Affiliations

School of Electrical and Computer Engineering, RMIT University, Melbourne, Australia


© G. R. Naik and D. K. Kumar. 2009

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.