Abstract
A maximum likelihood blind source separation algorithm is developed. The temporal dependencies are explained by assuming that each source is an AR process and the distribution of the associated i.i.d. innovations process is described using a Mixture of Gaussians (MOG). Unlike most maximum likelihood methods the proposed algorithm takes into account both spatial and temporal information, optimization is performed using the Expectation-Maximization method, and the source model is learned along with the demixing parameters.
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© 2006 Springer-Verlag Berlin Heidelberg
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Hild, K.E., Attias, H.T., Nagarajan, S.S. (2006). An EM Method for Spatio-temporal Blind Source Separation Using an AR-MOG Source Model. In: Rosca, J., Erdogmus, D., PrÃncipe, J.C., Haykin, S. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2006. Lecture Notes in Computer Science, vol 3889. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11679363_13
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DOI: https://doi.org/10.1007/11679363_13
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