Model-Independent Method of Nonlinear Blind Source Separation

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• 关键词：Blind source separation ; Nonlinear signal processing ; Invariants ; Sensor ; Analytic ; Model ; independent
• 刊名：Lecture Notes in Computer Science
• 出版年：2017
• 出版时间：2017
• 年：2017
• 卷：10169
• 期：1
• 页码：310-319
• 丛书名：Latent Variable Analysis and Signal Separation
• ISBN：978-3-319-53547-0
• 卷排序：10169

文摘

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.