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Empirical Wavelet Transform

IEEE Transactions on Signal Processing · 2013 · Vol. 61(16) · pp. 3999–4010
Jérôme Gilles

Abstract

Some recent methods, like the empirical mode decomposition (EMD), propose to decompose a signal accordingly to its contained information. Even though its adaptability seems useful for many applications, the main issue with this approach is its lack of theory. This paper presents a new approach to build adaptive wavelets. The main idea is to extract the different modes of a signal by designing an appropriate wavelet filter bank. This construction leads us to a new wavelet transform, called the empirical wavelet transform. Many experiments are presented showing the usefulness of this method compared to the classic EMD.

Machine Fault Diagnosis TechniquesImage and Signal Denoising MethodsFault Detection and Control SystemsWavelet transformComputer scienceWaveletSignal processingArtificial intelligenceMathematicsPattern recognition (psychology)TelecommunicationsRadar

Funding

  • National Science Foundation
  • W. M. Keck Foundation
  • Multidisciplinary University Research Initiative
  • Office of Naval Research
Citations
2,127
FWCI
61.62
field-weighted impact
References
17
Percentile
100%
vs. same field & year
Citations per year
Cited by
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Variational Mode Decomposition
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References
Entropy-based algorithms for best basis selection
IEEE Transactions on Information Theory · 1992 · 3,156 citations
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