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Adaptive Variational Nonlinear Chirp Mode Decomposition

Liang, Hao ; Ding, Xinghao ; Jakobsson, Andreas LU orcid ; Tu, Xiaotong LU orcid and Huang, Yue (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.

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author
; ; ; and
organization
publishing date
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}},
}