Abstract
The title of the paper refers to an extension of the classical blind source separation where the mixing of unknown sources is assumed in the form of convolution with impulse response of unknown linear dynamics. A further key assumption of our approach is that source signals are considered to be sparse with respect to a known dictionary, and thereby, an ℓ1-optimization is a natural formalism for solving the un-mixing problem.We demonstrate the effectiveness of the framework numerically.
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Georgiou, T.T., Tannenbaum, A. (2010). Sparse Blind Source Separation via ℓ1-Norm Optimization. In: Willems, J.C., Hara, S., Ohta, Y., Fujioka, H. (eds) Perspectives in Mathematical System Theory, Control, and Signal Processing. Lecture Notes in Control and Information Sciences, vol 398. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-93918-4_29
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DOI: https://doi.org/10.1007/978-3-540-93918-4_29
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