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IEEE Transactions on Signal Processing
Article . 2013 . Peer-reviewed
License: IEEE Copyright
Data sources: Crossref
https://dx.doi.org/10.48550/ar...
Article . 2024
License: CC BY NC ND
Data sources: Datacite
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Empirical Wavelet Transform

Authors: Jérôme Gilles;

Empirical Wavelet Transform

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.

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Keywords

Mathematics - Functional Analysis, Signal Processing (eess.SP), FOS: Mathematics, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Signal Processing, Functional Analysis (math.FA)

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2K
Top 0.01%
Top 0.1%
Top 1%
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