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arXiv · 2103.01352

Local Change Point Detection and Cleaning of EEMD Signals with Application to Acoustic Shockwaves

Kentaro Hoffman·Jonathan M. Lees·Kai Zhang
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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. The technique is implemented in R via the LCDSC package.

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Kentaro Hoffman, Jonathan M. Lees, Kai Zhang. 2021-03-01. Local Change Point Detection and Cleaning of EEMD Signals with Application to Acoustic Shockwaves. https://arxiv.org/abs/2103.01352

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