Empirical Wavelet Transform

By Jérôme Gilles. IEEE Transactions on Signal Processing, 2013

1,888 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/3803d42330506aff3a5cc5eaf69cf589c9c8929b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. A. Ben Abbes
2. Jinxing Liu
3. Wenming Zhang
4. Abhijit Bhattacharyya
5. Guang Meng
6. Beatriz Martínez
7. Chemseddine Rahmoune
8. Djamel Benazzouz

In Control and Systems Engineering

1. Wei Mo
2. Zhi Li
3. Heng Li
4. A. Ben Abbes
5. Jinxing Liu
6. Wenming Zhang
7. Abhijit Bhattacharyya
8. Guang Meng

Citations in the corpus

• Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review (2019, methodology): “The EWT is discussed in more details in [22].”
• Nonlinear Chirp Mode Decomposition: A Variational Method (2017, methodology): “In practice, if the signal is too noisy, one can still provide a relatively good initial IF by detecting spectrum peaks [22] or TF ridges [21], [29].”
• A Multivariate Approach for Patient-Specific EEG Seizure Detection Using Empirical Wavelet Transform (2017, methodology): “It should be noted that first (Ω0) and last (ΩN ) boundary frequencies are 0 and π, respectively [19].”
• A time varying filter approach for empirical mode decomposition (2017, methodology): “In fact, the result obtained using TVF-EMD is very similar to the EWT output, as shown in [23].”
• Feature Mode Decomposition: New Decomposition Theory for Rotating Machinery Fault Diagnosis (2023, neutral): “Motivated by this, Gilles [14] proposed empirical wavelet transform (EWT) whose principle is to build an adaptive wavelet basis to decompose the signal into adaptive subbands.”
• Early detection of tooth crack damage in gearbox using empirical wavelet transform combined by Hilbert transform (2017, methodology): “Empirical wavelet transform Gilles (2013) has introduced the EWT and tries to build adaptive wavelets capable of extracting a finite AM-FM components (modes) fk(t) of the signal f(t) such as: f ðtÞ ¼ XN k¼0 fk tð Þ ð1Þ To get the adaptability with respect to the analyzed signal, the Fourier…”
• Variational Mode Decomposition (2014, methodology): “In particular, we consider the same test signals that were previously suggested in [18] and also used in [14], with the purpose of increased comparability.”