IEEE Transactions on Signal Processing· 2013Q1
Empirical Wavelet Transform
- 2,180citations
- Q1SCImago
- 2013year
Short summary
A new 'empirical wavelet transform' (EWT) is introduced, which constructs adaptive wavelets by extracting signal modes via a designed wavelet filter bank.
AI-generated from the title and abstract; the full text is not read.
Key points
- Introduces the Empirical Wavelet Transform (EWT) as a new signal decomposition method.
- EWT builds adaptive wavelets using a designed wavelet filter bank to extract signal modes.
- The method offers a theoretical foundation lacking in previous adaptive techniques like EMD.
- Experimental results show EWT's usefulness and superiority over EMD.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. IEEE Transactions on Signal Processing, 2013 · DOI ↗
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Field: Control and Systems Engineering
Control and Systems EngineeringEngineering