Data-driven Signal Decomposition Approaches: A Comparative Analysis

TL;DR

This paper compares various data-driven signal decomposition methods like EMD, VMD, SST, and SSA, evaluating their accuracy, robustness to noise, and parameter sensitivity through extensive experiments on synthetic and real signals.

Contribution

It provides a comprehensive comparative analysis of popular SD algorithms, highlighting their strengths, weaknesses, and best practices for parameter selection.

Findings

VMD and SST show robustness to noise in multivariate signals.

EMD is sensitive to parameter changes but effective for certain applications.

Best practices for parameter tuning improve algorithm performance.

Abstract

Signal decomposition (SD) approaches aim to decompose non-stationary signals into their constituent amplitude- and frequency-modulated components. This represents an important preprocessing step in many practical signal processing pipelines, providing useful knowledge and insight into the data and relevant underlying system(s) while also facilitating tasks such as noise or artefact removal and feature extraction. The popular SD methods are mostly data-driven, striving to obtain inherent well-behaved signal components without making many prior assumptions on input data. Among those methods include empirical mode decomposition (EMD) and variants, variational mode decomposition (VMD) and variants, synchrosqueezed transform (SST) and variants and sliding singular spectrum analysis (SSA). With the increasing popularity and utility of these methods in wide-ranging application, it is imperative to gain a better understanding and insight into the operation of these algorithms, evaluate their accuracy with and without noise in input data and gauge their sensitivity against algorithmic parameter changes. In this work, we achieve those tasks through extensive experiments involving carefully designed synthetic and real-life signals. Based on our experimental observations, we comment on the pros and cons of the considered SD algorithms as well as highlighting the best practices, in terms of parameter selection, for the their successful operation. The SD algorithms for both single- and multi-channel (multivariate) data fall within the scope of our work. For multivariate signals, we evaluate the performance of the popular algorithms in terms of fulfilling the mode-alignment property, especially in the presence of noise.