Synchrosqueezed Wavelet Transforms: a Tool for Empirical Mode Decomposition

TL;DR

This paper introduces a mathematically rigorous method inspired by EMD that effectively decomposes signals into harmonic components, validated through simulations and real data.

Contribution

It provides a new precise mathematical framework for signal decomposition inspired by EMD, with proven success on various data types.

Findings

Successfully decomposes signals into harmonic components

Proven mathematically to work within a defined function class

Validated with simulated and real-world data

Abstract

The EMD algorithm, first proposed in [11], made more robust as well as more versatile in [12], is a technique that aims to decompose into their building blocks functions that are the superposition of a (reasonably) small number of components, well separated in the time-frequency plane, each of which can be viewed as approximately harmonic locally, with slowly varying amplitudes and frequencies. The EMD has already shown its usefulness in a wide range of applications including meteorology, structural stability analysis, medical studies -- see, e.g. [13]. On the other hand, the EMD algorithm contains heuristic and ad-hoc elements that make it hard to analyze mathematically. In this paper we describe a method that captures the flavor and philosophy of the EMD approach, albeit using a different approach in constructing the components. We introduce a precise mathematical definition for a class of functions that can be viewed as a superposition of a reasonably small number of approximately harmonic components, and we prove that our method does indeed succeed in decomposing arbitrary functions in this class. We provide several examples, for simulated as well as real data.