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I tried to apply a MATLAB Empirical Mode Decomposition routine to denoise a signal, basically retaining only the last IMFs, with a criterion based on the mode energy.

To validate the routine, I have built a synthetic signal with added Gaussian noise + a sinusoidal disturbance. I noticed that the EMD routine (at least mine) seems very sensitive to the noise. In fact, if I launch it twice, the generated noise is of course different and the IMFs are also quite different.

Do you have any suggestions on how to "stabilize" the routine?

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Indeed, at least in my experience, computing IMFs can be sensitive to borders, impulse signals and noise realizations. As you are interested in wavelets, note that in Empirical mode decomposition as a filter bank a link is made with DWT:

we report here on numerical experiments based on fractional Gaussian noise. In such a case, it turns out that EMD acts essentially as a dyadic filter bank resembling those involved in wavelet decompositions

Finally, there are regularized EMD schemes than apparently stabilize EMD decomposition, see for instance: A multicomponent proximal algorithm for empirical mode decomposition and related papers by this team.

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I'm sure you came across Ensemble. Spr EMD.

I forget where I read this (not my idea) but I have in my head a technique that is basically to add a higher frequency sine wave on top of your signal.

The frequency of wave you add needs to be far from frequency of interest. Speculating as to how this could help I would say the high freq content of your added noise gets pulled toward this added tone by the EMD dyadic nature.

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  • $\begingroup$ I have one point: does the EEMD also reduce the sensitivity to perturbations or does it only reduce the "mode mixing" problems? $\endgroup$ – EmThorns Oct 3 at 17:18

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