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Local Change Point Detection and Cleaning of EEMD Signals

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Abstract

The ensemble empirical mode decomposition (EEMD) has become a preferred technique to decompose nonlinear and non-stationary signals due to its ability to create time-varying basis functions. However, current EEMD signal cleaning techniques are unable to deal with situations where a signal only occurs for a portion of the entire recording length. By combining change point detection and statistical hypothesis testing, we demonstrate how to clean a signal to emphasize unique local changes within each basis function. This not only allows us to observe which frequency bands are undergoing a change, but also leads to improved recovery of the underlying information. Using this technique, we demonstrate improved signal cleaning performance for acoustic shockwave signal detection.

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Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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Acknowledgements

This work was supported by the National Science Foundation under Grant Nos. DMS-1613112, IIS-1633212, DMS-1916237, DMS-1929298, and DMS-2152289. All authors have no competing interests to declare that are relevant to the content of this article.

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Authors and Affiliations

  1. Whiting School of Engineering, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD, 21218-2681, USA

    Kentaro Hoffman

  2. Department of Earth, Marine, and Environmental Sciences, University of North Carolina at Chapel Hill, 313 Mitchell Hall, Chapel Hill, NC, 27599, USA

    Jonathan Lees

  3. Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, 318 Hanes Hall, Chapel Hill, NC, 27514, USA

    Kai Zhang

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  1. Kentaro Hoffman
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Correspondence to Kentaro Hoffman.

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Hoffman, K., Lees, J. & Zhang, K. Local Change Point Detection and Cleaning of EEMD Signals. Circuits Syst Signal Process 42, 4669–4690 (2023). https://doi.org/10.1007/s00034-023-02319-0

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