Noise-modulated empirical mode decomposition

Po Hsiang Tsui, Chien Cheng Chang, Norden E. Huang

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


The empirical mode decomposition (EMD) is the core of the Hilbert-Huang transform (HHT). In HHT, the EMD is responsible for decomposing a signal into intrinsic mode functions (IMFs) for calculating the instantaneous frequency and eventually the Hilbert spectrum. The EMD method as originally proposed, however, has an annoying mode mixing problem caused by the signal intermittency, making the physical interpretation of each IMF component unclear. To resolve this problem, the ensemble EMD (EEMD) was subsequently developed. Unlike the conventional EMD, the EEMD defines the true IMF components as the mean of an ensemble of trials, each consisting of the signal with added white noise of finite, not infinitesimal, amplitude. In this study, we further proposed an extension and alternative to EEMD designated as the noise-modulated EMD (NEMD). NEMD does not eliminate mode but intensify and amplify mixing by suppressing the small amplitude signal but the larger signals would be preserved without waveform deformation. Thus, NEMD may serve as a new adaptive threshold amplitude filtering. The principle, algorithm, simulations, and applications are presented in this paper. Some limitations and additional considerations of using the NEMD are also discussed.

Original languageEnglish
Pages (from-to)25-37
Number of pages13
JournalAdvances in Adaptive Data Analysis
Issue number1
StatePublished - Jan 2010


  • Empirical mode decomposition (EMD)
  • ensemble empirical mode decomposition (EEMD)
  • noise-modulated empirical mode decomposition (NEMD)


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