Summary
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, which suggests a mixed L 1/L 2-optimization as a possible formalism for solving the un-mixing problem. We demonstrate the effectiveness of the framework numerically.
This work was supported in part by grants from NSF, AFOSR, ARO, as well as by NIH (NAC P41 RR-13218) through Brigham and Women’s Hospital. This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on the National Centers for Biomedical Computing can be obtained from http://nihroadmap.nih.gov/bioinformatics.
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Georgiou, T.T., Tannenbaum, A. (2010). Sparse Blind Source Deconvolution with Application to High Resolution Frequency Analysis. In: Hu, X., Jonsson, U., Wahlberg, B., Ghosh, B. (eds) Three Decades of Progress in Control Sciences. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11278-2_13
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DOI: https://doi.org/10.1007/978-3-642-11278-2_13
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