Abstract
Consider a time series of signal measurements x(t), where x has two components. This paper shows how to process the local distributions of measurement velocities in order to construct a two-component mapping, u(x). If the measurements are linear or nonlinear combinations of statistically independent variables, u(x) must be an unmixing function. In other words, the measurement data are separable if and only if \(u_{1}[x(t)]\) and \(u_{2}[x(t)]\) are statistically independent of one another. The method is analytic, constructive, and model-independent. It is illustrated by blindly recovering the separate utterances of two speakers from nonlinear combinations of their waveforms.
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Levin, D.N. (2017). Model-Independent Method of Nonlinear Blind Source Separation. In: Tichavský, P., Babaie-Zadeh, M., Michel, O., Thirion-Moreau, N. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2017. Lecture Notes in Computer Science(), vol 10169. Springer, Cham. https://doi.org/10.1007/978-3-319-53547-0_30
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DOI: https://doi.org/10.1007/978-3-319-53547-0_30
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