Fully adaptive mode decomposition from time-frequency ridges

Sylvain Meignen, T. Oberlin, Stephen McLaughlin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In this paper, we consider ridge detection for multicomponent signal analysis. We introduce a new ridge detector based on a projection of the reassignment vector in a specific direction which is related to the geometry of the spectrogram magnitude. The ridge definition we introduce enables that of the basin of attraction associated with a ridge and then mode reconstruction. Simulations show better concentration of the information on the ridges obtained by our method compared to other existing ridge detectors that also make use of the reassignment vector.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherIEEE
Pages3884-3888
Number of pages5
ISBN (Electronic)9781509041176
DOIs
Publication statusPublished - 19 Jun 2017
Event42nd IEEE International Conference on Acoustics, Speech, and Signal Processing 2017 - New Orleans, United States
Duration: 5 Mar 20179 Mar 2017

Publication series

NameIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherIEEE
ISSN (Electronic)2379-190X

Conference

Conference42nd IEEE International Conference on Acoustics, Speech, and Signal Processing 2017
Abbreviated titleICASSP 2017
CountryUnited States
CityNew Orleans
Period5/03/179/03/17

Keywords

  • AM/FM
  • multicomponent signals
  • reassignment
  • ridges
  • short-time Fourier transform
  • time-frequency

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Meignen, S., Oberlin, T., & McLaughlin, S. (2017). Fully adaptive mode decomposition from time-frequency ridges. In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 3884-3888). (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)). IEEE. https://doi.org/10.1109/ICASSP.2017.7952884