Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Determining Number of Independent Sources in Undercomplete Mixture | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2009 Article ID 694850 5 pages doi 2009 694850 Research Article Determining Number of Independent Sources in Undercomplete Mixture Ganesh R. Naik and Dinesh K. Kumar School of Electrical and Computer Engineering RMIT University GPO Box 2476V Melbourne VIC 3001 Australia Correspondence should be addressed to Ganesh R. Naik Received 14 March 2009 Revised 28 July 2009 Accepted 2 September 2009 Recommended by Shoji Makino 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 mixlure 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. Copyright 2009 G. R. Naik and D. K. Kumar. 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. 1. Introduction Blind Source Separation BSS consists of estimating the original signals s from a finite set of observations x when x is a result of mixing the original signals s. The estimation is done without any prior .
