Empirical Wavelet Transform

TLDR

Empirical Mode Decomposition (EMD) methods decompose signals adaptively but lack a solid theoretical foundation. This paper introduces a new approach for constructing adaptive wavelets. The authors extract signal modes by designing a tailored wavelet filter bank. The resulting empirical wavelet transform outperforms classic EMD in several experiments.

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.