  With the ability to create time varying basis functions, the Ensemble
Empirical Mode Decomposition (EEMD) has quickly become the preferred way to
decompose nonlinear and nonstationary signals. However, we find current EEMD
signal cleaning techniques lacking, unable to deal with the nonlinearities that
are common for the complex signals that the EEMD is used for. By combining
change point detection and a new sparse basis function optimization problem, we
are able to show that it is possible to create unique filters for each change
point which emphasize the basis functions that are observing a change. This not
only allows one to understand which frequency bands are observing a change, but
cleaning the signal to emphasize changes can lead to improved signal
classification accuracy. We show that this technique has implications for a
variety of applications including acoustics and medicine. The technique is
implemented in R via the \textbf{LCDSC} package.
