  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.

Signal Processing