Nonstationary signal decomposition using quadratic time–frequency distributions
| dc.contributor.author | O'Toole, J. M. | en |
| dc.contributor.author | Stevenson, N. J. | en |
| dc.contributor.funder | Science Foundation Ireland | en |
| dc.date.accessioned | 2025-07-17T14:43:55Z | |
| dc.date.available | 2025-07-17T14:43:55Z | |
| dc.date.issued | 2025-07-12 | en |
| dc.description.abstract | Extracting and isolating individual components of a multi-component signal is an important but challenging task in signal processing. Signal components can be buried in noise, time-limited without temporal overlap, and can have minimal separation in the frequency domain. This work presents two methods for decomposing multi-component signals that overcome the limitations of separating signal energy, jointly, in both time and frequency domains. These methods use established peak-tracking algorithms to extract instantaneous frequency (IF) laws from contiguous regions of local-energy concentrations in a quadratic time–frequency distribution (TFD). The novelty of this work is in the methods that convert these IF laws into signal components using either (1) time-varying filter banks or (2) sinusoidal model fitting with cross-TFD phase corrections. We validate these methods using simulated and real-world signals and compare their performance against existing techniques such as the time-varying empirical mode decomposition, variational mode decomposition, and synchrosqueezed transforms. The proposed methods achieve superior efficacy for decomposition over a range of nonstationary signals, do not require a priori knowledge, such as centre frequencies or component number, and perform well in the presence of noise. | en |
| dc.description.sponsorship | Science Foundation Ireland (15/SIRG/3580) | en |
| dc.description.status | Peer reviewed | en |
| dc.description.version | Published Version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.articleid | 110095 | en |
| dc.identifier.citation | O'Toole, J. M. and Stevenson, N. J. (2025) 'Nonstationary signal decomposition using quadratic time–frequency distributions', Signal Processing, 238, 110095 (11pp). https://doi.org/10.1016/j.sigpro.2025.110095 | en |
| dc.identifier.doi | 10.1016/j.sigpro.2025.110095 | en |
| dc.identifier.issn | 1651684 | en |
| dc.identifier.journaltitle | Signal Processing | en |
| dc.identifier.uri | https://hdl.handle.net/10468/17719 | |
| dc.identifier.volume | 238 | |
| dc.language.iso | en | en |
| dc.publisher | Elsevier B.V. | en |
| dc.relation.project | 15/SIRG/3580 | en |
| dc.rights | © 2025, the Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). | en |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Cross-time–frequency distributions | en |
| dc.subject | Quadratic time–frequency distributions | en |
| dc.subject | Signal decomposition | en |
| dc.subject | Signal modelling | en |
| dc.subject | Time-varying filtering | en |
| dc.title | Nonstationary signal decomposition using quadratic time–frequency distributions | en |
| dc.type | Article (peer reviewed) | en |