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- Title
Local Change Point Detection and Cleaning of EEMD Signals.
- Authors
Hoffman, Kentaro; Lees, Jonathan; Zhang, Kai
- 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.
- Subjects
CHANGE-point problems; DATA scrubbing; ACOUSTIC signal detection; HILBERT-Huang transform; STATISTICAL hypothesis testing; CLEANING; ACOUSTIC emission testing
- Publication
Circuits, Systems & Signal Processing, 2023, Vol 42, Issue 8, p4669
- ISSN
0278-081X
- Publication type
Article
- DOI
10.1007/s00034-023-02319-0