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EURASIP Journal on Advances in Signal Processing
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Determining Number of Independent Sources in Undercomplete Mixture

EURASIP Journal on Advances in Signal Processing volume 2009, Article number: 694850 (2009) Cite this article

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.

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  1. School of Electrical and Computer Engineering, RMIT University, GPO Box 2476V, Melbourne, VIC, 3001, Australia

    Ganesh R. Naik & Dinesh K. Kumar

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  1. Ganesh R. Naik
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  2. Dinesh K. Kumar
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Correspondence to Ganesh R. Naik.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Naik, G.R., Kumar, D.K. Determining Number of Independent Sources in Undercomplete Mixture. EURASIP J. Adv. Signal Process. 2009, 694850 (2009). https://doi.org/10.1155/2009/694850

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  • DOI: https://doi.org/10.1155/2009/694850

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