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

TL;DR

This paper introduces a novel method combining change point detection and hypothesis testing to improve local signal cleaning in EEMD signals, particularly for partial recordings, enhancing acoustic shockwave detection.

Contribution

It presents a new approach for local signal cleaning in EEMD by integrating change point detection, enabling better handling of signals that occur only partially.

Findings

Enhanced detection of local changes in EEMD signals

Improved recovery of underlying information in acoustic shockwaves

Effective signal cleaning for partial recordings

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.