Abstract
In this paper, a blind source separation method for bilinear mixtures of two source signals is presented, that relies on nonlinear correlation between separating system outputs. An estimate of each source is created by linearly combining observed mixtures and maximizing a cost function based on the correlation between the element-wise product of the estimated sources and the corresponding quadratic term. A proof of the method separability, i.e. of the uniqueness of the solution to the cost function maximization problem, is also given. The algorithm used in this work is also presented. Its effectiveness is demonstrated through tests with artificial mixtures created with real Earth observation spectra. The proposed method is shown to yield much better performance than a state-of-the-art method.
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Guerrero, A., Deville, Y., Hosseini, S. (2018). A Blind Source Separation Method Based on Output Nonlinear Correlation for Bilinear Mixtures. In: Deville, Y., Gannot, S., Mason, R., Plumbley, M., Ward, D. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2018. Lecture Notes in Computer Science(), vol 10891. Springer, Cham. https://doi.org/10.1007/978-3-319-93764-9_18
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DOI: https://doi.org/10.1007/978-3-319-93764-9_18
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