Determining Number of Independent Sources in Undercomplete Mixture. (28th September 2009)

• Record Type: Journal Article
• Authors: Naik, Ganesh R.; Kumar, Dinesh K.
• Other Names: Makino Shoji Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2009(2009)
• Publication Date: 2009-09-28
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2009/694850
• Languages: English
• ISSNs: 1687-6172

Abstract

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 ofM recordings. It has also been shown that performing ICA onN randomly selected recordings out ofM recordings gives good quality of separation.