Abstract
In this work, we deal with the problem of nonlinear blind source separation (BSS). We propose a new method for BSS in overdetermined linear-quadratic (LQ) mixtures. By exploiting the assumption that the sources are sparse in a transformed domain, we define a framework for canceling the nonlinear part of the mixing process. After that, separation can be conducted by linear BSS algorithms. Experiments with synthetic data are performed to assess the viability of our proposal.
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Duarte, L.T., Ando, R.A., Attux, R., Deville, Y., Jutten, C. (2012). Separation of Sparse Signals in Overdetermined Linear-Quadratic Mixtures. In: Theis, F., Cichocki, A., Yeredor, A., Zibulevsky, M. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2012. Lecture Notes in Computer Science, vol 7191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28551-6_30
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DOI: https://doi.org/10.1007/978-3-642-28551-6_30
Publisher Name: Springer, Berlin, Heidelberg
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