2018
DOI: 10.48550/arxiv.1806.08739
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Data-driven Spatiotemporal Modal Decomposition for Time Frequency Analysis

Abstract: We propose a new solution to the blind source separation problem that factors mixed time-series signals into a sum of spatiotemporal modes, with the constraint that the temporal components are intrinsic mode functions (IMF's). The key motivation is that IMF's allow the computation of meaningful Hilbert transforms of non-stationary data, from which instantaneous time-frequency representations may be derived. Our spatiotemporal intrinsic mode decomposition (STIMD) method leverages spatial correlations to general… Show more

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References 37 publications
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