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• W2737072408 abstract "Separating statistically dependent source signals from their linear mixtures is a challenging problem in signal processing society. Firstly, we show that maximization of the non-Gaussianity (NG) measure among the separated signals can realize dependent source signals separation. Then, based on cumulative distribution function (CDF) instead of traditional probability density function (PDF), the NG measure is defined by utilizing statistical distances between different distributions. After that, the CDF based objective function is estimated by utilizing nonparametric order statistics (OS). At last, by consulting the stochastic gradient rule of constrained optimization problem, the efficiently nonparametric dependent sources separation algorithm is derived and termed as nonpNG. Simulation results demonstrate the validity of the proposed statistically dependent sources separation algorithm." @default.
• W2737072408 created "2017-07-31" @default.
• W2737072408 creator A5049446746 @default.
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• W2737072408 date "2017-01-01" @default.
• W2737072408 modified "2023-09-23" @default.
• W2737072408 title "Dependent Source Separation with Nonparametric Non-Gaussianity Measure" @default.
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• W2737072408 doi "https://doi.org/10.1007/978-3-319-63312-1_8" @default.
• W2737072408 hasPublicationYear "2017" @default.
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