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Data-driven Signal Decomposition Approaches: A Comparative Analysis
Signal Processing · Submitted 23 Aug 2022
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Signal decomposition (SD) approaches aim to decompose non-stationary signals into their constituent amplitude- and frequency-modulated components. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | 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. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s2 | The popular SD methods are mostly data-driven, striving to obtain inherent well-behaved signal components without making many prior assumptions on input data. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s3 | 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). | background 1.00 | 0.04 | 0.02 | asserted 0.99 |
| s4 | 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. | gap 0.63 | 0.05 | 0.02 | asserted 0.97 |
| s5 | In this work, we achieve those tasks through extensive experiments involving carefully designed synthetic and real-life signals. | method 0.91 | 0.94 | 0.03 | asserted 1.00 |
| s6 | 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. | contribution 0.44 | 0.93 | 0.49 | asserted 0.99 |
| s7 | The SD algorithms for both single- and multi-channel (multivariate) data fall within the scope of our work. | contribution 0.79 | 0.35 | 0.03 | asserted 1.00 |
| s8 | 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. | contribution 0.54 | 0.90 | 0.06 | asserted 0.99 |
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Original 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.
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2 arXiv papers cite this in 4 sentences
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Cites it as background 2 papers
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Recently, extensions of these methods for multivariate signals have also emerged, namely multivariate extensions of empirical mode decomposition (MEMD) [ref] , variational mode decomposition (MVMD) [ref] and iterative filtering [ref] ; a recent article provides an overview of both univariate and multivariate signal decomposition methods along with their comparative analysis [ref] .
Time-Varying Graph Mode Decomposition · Introduction · 0.84
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A more comprehensive comparison of both univariate and multivariate approaches is presented in [ref] .
Latent Mode Decomposition · Introduction · 0.86
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