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arXiv · 2105.06958

A Hypothesis Testing Approach to Nonstationary Source Separation

Reza Sameni·Christian Jutten
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Abstract

The extraction of nonstationary signals from blind and semi-blind multivariate observations is a recurrent problem. Numerous algorithms have been developed for this problem, which are based on the exact or approximate joint diagonalization of second or higher order cumulant matrices/tensors of multichannel data. While a great body of research has been dedicated to joint diagonalization algorithms, the selection of the diagonalized matrix/tensor set remains highly problem-specific. Herein, various methods for nonstationarity identification are reviewed and a new general framework based on hypothesis testing is proposed, which results in a classification/clustering perspective to semi-blind source separation of nonstationary components. The proposed method is applied to noninvasive fetal ECG extraction, as case study.

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Reza Sameni, Christian Jutten. 2021-06-28. A Hypothesis Testing Approach to Nonstationary Source Separation. https://doi.org/10.1109/ssp49050.2021.9513811

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