# Empirical wavelet transform

arXiv: https://arxiv.org/abs/2410.23534 · HTML: https://mlsanity.com/paper/2410.23534/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2024.

## Built on by (most cited first, 6 of 6)

- [Rational Gaussian wavelets and corresponding model driven neural networks](https://mlsanity.com/paper/2502.01282/) (2025)
- [Variational decomposition autoencoding improves disentanglement of latent representations](https://mlsanity.com/paper/2601.06844/) (2026)
- [Engineering Carbon Credits Towards A Responsible FinTech Era: The Practices, Implications, and Future](https://mlsanity.com/paper/2501.14750/) (2024)
- [Degradation Self-Supervised Learning for Lithium-ion Battery Health Diagnostics](https://mlsanity.com/paper/2503.08083/) (2025)
- [Tourist arrival forecasting with game-theoretic social influence weighting and deep learning](https://mlsanity.com/paper/2507.03411/) (2025)
- [xTime: Extreme Event Prediction with Hierarchical Knowledge Distillation and Expert Fusion](https://mlsanity.com/paper/2510.20651/) (2025)

## Abstract (arXiv)

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.