Adaptive Variational Nonlinear Chirp Mode Decomposition
(2022) 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings 2022-May. p.5632-5636- Abstract
Variational nonlinear chirp mode decomposition (VNCMD) is a recently introduced method for nonlinear chirp signal decomposition that has aroused notable attention in various fields. One limiting aspect of the method is that its performance relies heavily on the setting of the bandwidth parameter. To overcome this problem, we here propose a Bayesian implementation of the VNCMD, which can adaptively estimate the instantaneous amplitudes and frequencies of the nonlinear chirp signals, and then learn the active dictionary in a data-driven manner, thereby enabling a high-resolution time-frequency representation. Numerical example of both simulated and measured data illustrate the resulting improvement performance of the proposed method.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/2b9807c3-f19a-41ea-9b3a-224e3a53f235
- author
- Liang, Hao ; Ding, Xinghao ; Jakobsson, Andreas LU ; Tu, Xiaotong LU and Huang, Yue
- organization
- publishing date
- 2022
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- adaptive estimation, mode decomposition, Nonlinear chirp signal, time-frequency analysis
- host publication
- 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
- series title
- ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
- volume
- 2022-May
- pages
- 5 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
- conference location
- Virtual, Online, Singapore
- conference dates
- 2022-05-23 - 2022-05-27
- external identifiers
-
- scopus:85131253147
- ISSN
- 1520-6149
- ISBN
- 9781665405409
- DOI
- 10.1109/ICASSP43922.2022.9746147
- language
- English
- LU publication?
- yes
- id
- 2b9807c3-f19a-41ea-9b3a-224e3a53f235
- date added to LUP
- 2022-08-19 15:57:17
- date last changed
- 2023-11-21 02:29:25
@inproceedings{2b9807c3-f19a-41ea-9b3a-224e3a53f235, abstract = {{<p>Variational nonlinear chirp mode decomposition (VNCMD) is a recently introduced method for nonlinear chirp signal decomposition that has aroused notable attention in various fields. One limiting aspect of the method is that its performance relies heavily on the setting of the bandwidth parameter. To overcome this problem, we here propose a Bayesian implementation of the VNCMD, which can adaptively estimate the instantaneous amplitudes and frequencies of the nonlinear chirp signals, and then learn the active dictionary in a data-driven manner, thereby enabling a high-resolution time-frequency representation. Numerical example of both simulated and measured data illustrate the resulting improvement performance of the proposed method.</p>}}, author = {{Liang, Hao and Ding, Xinghao and Jakobsson, Andreas and Tu, Xiaotong and Huang, Yue}}, booktitle = {{2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings}}, isbn = {{9781665405409}}, issn = {{1520-6149}}, keywords = {{adaptive estimation; mode decomposition; Nonlinear chirp signal; time-frequency analysis}}, language = {{eng}}, pages = {{5632--5636}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, series = {{ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings}}, title = {{Adaptive Variational Nonlinear Chirp Mode Decomposition}}, url = {{http://dx.doi.org/10.1109/ICASSP43922.2022.9746147}}, doi = {{10.1109/ICASSP43922.2022.9746147}}, volume = {{2022-May}}, year = {{2022}}, }