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The Ensemble Empirical Mode Decomposition (EEMD) has become a preferredtechnique to decompose nonlinear and non-stationary signals due to its abilityto create time-varying basis functions. However, current EEMD signal cleaningtechniques are unable to deal with situations where a signal only occurs for aportion of the entire recording length. By combining change point detection andstatistical hypothesis testing, we demonstrate how to clean a signal toemphasize unique local changes within each basis function. This not only allowsus to observe which frequency bands are undergoing a change, but also leads toimproved recovery of the underlying information. Using this technique, wedemonstrate improved signal cleaning performance for acoustic shockwave signaldetection. The technique is implemented in R via the LCDSC package.

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