Department of Electrical and Computer Engineering, Aarhus University, Finlandsgade 22, 8200, Aarhus, Denmark.
Sci Rep. 2023 Jan 31;13(1):1798. doi: 10.1038/s41598-023-28390-w.
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 and variants, variational mode decomposition and variants, synchrosqueezed transform and variants and sliding singular spectrum analysis. With the increasing popularity and utility of these methods in wide-ranging applications, 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.
信号分解（SD）方法旨在将非平稳信号分解为其组成的幅度和频率调制分量。这是许多实际信号处理管道中的一个重要预处理步骤，为数据和相关基础系统提供了有用的知识和见解，同时还促进了噪声或伪影去除和特征提取等任务。流行的 SD 方法大多是数据驱动的，旨在获得固有表现良好的信号分量，而不对输入数据做出许多先验假设。其中包括经验模态分解及其变体、变分模态分解及其变体、同步挤压变换及其变体以及滑动奇异谱分析。随着这些方法在广泛应用中的日益普及和实用性，深入了解和洞察这些算法的运作、评估它们在输入数据中有无噪声时的准确性以及衡量它们对算法参数变化的敏感性变得至关重要。在这项工作中，我们通过涉及精心设计的合成和真实信号的广泛实验来实现这些任务。基于我们的实验观察，我们对所考虑的 SD 算法的优缺点进行了评论，并强调了成功操作的最佳实践，包括参数选择。我们的工作范围包括单通道和多通道（多变量）数据的 SD 算法。对于多变量信号，我们评估了流行算法在满足模式对齐属性方面的性能，特别是在存在噪声的情况下。
