Abstract
This paper deals with the problem of Blind Source Separation. Contrary to the vast majority of works, we do not assume the statistical independence between the sources and explicitly consider that they are dependent. We introduce three particular models of dependent sources and show that their cumulants have interesting properties. Based on these properties, we investigate the behaviour of classical Blind Source Separation algorithms when applied to these sources: depending on the source vector, the separation may be sucessful or some additionnal indeterminacies can be identified.
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Castella, M., Comon, P. (2007). Blind Separation of Instantaneous Mixtures of Dependent Sources. In: Davies, M.E., James, C.J., Abdallah, S.A., Plumbley, M.D. (eds) Independent Component Analysis and Signal Separation. ICA 2007. Lecture Notes in Computer Science, vol 4666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74494-8_2
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DOI: https://doi.org/10.1007/978-3-540-74494-8_2
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