Empirical wavelet transform

TL;DR

This paper introduces the empirical wavelet transform, a new adaptive wavelet method inspired by EMD, designed to decompose signals based on their intrinsic modes, supported by experimental validation.

Contribution

The paper proposes a novel wavelet transform that adaptively decomposes signals into modes using a wavelet filter bank, addressing the lack of theoretical foundation in EMD.

Findings

Empirical wavelet transform effectively decomposes signals into meaningful modes.

The method outperforms traditional EMD in various experiments.

Experimental results demonstrate improved adaptability and robustness.

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.