A blind source separation method is described to extract sources from data mixtures where the underlying sources are sparse and\r\ncorrelated. The approach used is to detect and analyze segments of time where one source exists on its own. The method does not\r\nassume independence of sources and probability density functions are not assumed for any of the sources. A comparison is made\r\nbetween the proposed method and the Fast-ICA and Clusterwise PCA methods. It is shown that the proposed method works best\r\nfor cases where the underlying sources are strongly correlated because Fast-ICA assumes zero correlation between sources and\r\nClusterwise PCA can be sensitive to overlap between sources. However, for cases of sources that are sparse and weakly correlated\r\nwith each other, there is a tendency for Fast-ICA and Clusterwise PCA to have better performances than the proposedmethod, the\r\nreason being that these methods appear to be more robust to changes in input parameters to the algorithms. In addition, because\r\nof the deflationary nature of the proposed method, there is a tendency for estimates to be more affected by noise than Fast-ICA\r\nwhen the number of sources increases. The paper concludes with a discussion concerning potential applications for the proposed\r\nmethod.
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