Abstract
In this work, we investigate the use of monotonic neural networks as compensating functions in the context of source separation of post-nonlinear (PNL) mixtures. We first provide a numerical example that illustrates the importance of having bijective nonlinear compensating functions in PNL models. Then, we propose a separation framework in which a monotonic neural network is considered in the first stage of the PNL separating system. Finally, numerical experiments are performed to assess the proposed framework.
The authors would like to thank FAPESP and CNPq for funding this research.
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Notes
- 1.
We here keep the index i, which is related to the mixtures, and also the sample index n.
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Duarte, L.T., de Oliveira Pereira, F., Attux, R., Suyama, R., Romano, J.M.T. (2015). Source Separation in Post-nonlinear Mixtures by Means of Monotonic Networks. In: Vincent, E., Yeredor, A., Koldovský, Z., Tichavský, P. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2015. Lecture Notes in Computer Science(), vol 9237. Springer, Cham. https://doi.org/10.1007/978-3-319-22482-4_20
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DOI: https://doi.org/10.1007/978-3-319-22482-4_20
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