Multicomponent signal denoising with synchrosqueezing

S. Meignen; T. Oberlin; Stephen McLaughlin

doi:10.1109/SSP.2012.6319788

Multicomponent signal denoising with synchrosqueezing

S. Meignen^*
, T. Oberlin
, Stephen McLaughlin

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 Citations (Scopus)

Abstract

In this paper, we develop a new technique based on the synchrosqueezing method to denoise multicomponent signals. The approach proposed is based on a two step strategy: a mode detection step followed by a reconstruction one. The emphasis is put on the robustness of the detection step in a noisy context, a key issue in the implementation of the method. Numerical applications show the improvement in terms of multicomponent signal denoising brought about by the proposed method over the translation-invariant wavelet thresholding.

Original language	English
Title of host publication	2012 IEEE Statistical Signal Processing Workshop (SSP)
Publisher	IEEE
Pages	660-663
Number of pages	4
ISBN (Print)	9781467301831
DOIs	https://doi.org/10.1109/SSP.2012.6319788
Publication status	Published - 4 Oct 2012
Event	2012 IEEE Statistical Signal Processing Workshop - Ann Arbor, MI, United States Duration: 5 Aug 2012 → 8 Aug 2012

Conference

Conference	2012 IEEE Statistical Signal Processing Workshop
Abbreviated title	SSP 2012
Country/Territory	United States
City	Ann Arbor, MI
Period	5/08/12 → 8/08/12

Keywords

Denoising
Synchrosqueezing
Time-Frequency
Wavelets

ASJC Scopus subject areas

Signal Processing

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10.1109/SSP.2012.6319788

Cite this

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title = "Multicomponent signal denoising with synchrosqueezing",

abstract = "In this paper, we develop a new technique based on the synchrosqueezing method to denoise multicomponent signals. The approach proposed is based on a two step strategy: a mode detection step followed by a reconstruction one. The emphasis is put on the robustness of the detection step in a noisy context, a key issue in the implementation of the method. Numerical applications show the improvement in terms of multicomponent signal denoising brought about by the proposed method over the translation-invariant wavelet thresholding.",

keywords = "Denoising, Synchrosqueezing, Time-Frequency, Wavelets",

author = "S. Meignen and T. Oberlin and Stephen McLaughlin",

year = "2012",

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doi = "10.1109/SSP.2012.6319788",

language = "English",

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pages = "660--663",

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TY - GEN

T1 - Multicomponent signal denoising with synchrosqueezing

AU - Meignen, S.

AU - Oberlin, T.

AU - McLaughlin, Stephen

PY - 2012/10/4

Y1 - 2012/10/4

N2 - In this paper, we develop a new technique based on the synchrosqueezing method to denoise multicomponent signals. The approach proposed is based on a two step strategy: a mode detection step followed by a reconstruction one. The emphasis is put on the robustness of the detection step in a noisy context, a key issue in the implementation of the method. Numerical applications show the improvement in terms of multicomponent signal denoising brought about by the proposed method over the translation-invariant wavelet thresholding.

AB - In this paper, we develop a new technique based on the synchrosqueezing method to denoise multicomponent signals. The approach proposed is based on a two step strategy: a mode detection step followed by a reconstruction one. The emphasis is put on the robustness of the detection step in a noisy context, a key issue in the implementation of the method. Numerical applications show the improvement in terms of multicomponent signal denoising brought about by the proposed method over the translation-invariant wavelet thresholding.

KW - Denoising

KW - Synchrosqueezing

KW - Time-Frequency

KW - Wavelets

UR - https://www.scopus.com/pages/publications/84868243725

U2 - 10.1109/SSP.2012.6319788

DO - 10.1109/SSP.2012.6319788

M3 - Conference contribution

AN - SCOPUS:84868243725

SN - 9781467301831

SP - 660

EP - 663

BT - 2012 IEEE Statistical Signal Processing Workshop (SSP)

PB - IEEE

T2 - 2012 IEEE Statistical Signal Processing Workshop

Y2 - 5 August 2012 through 8 August 2012

ER -

Multicomponent signal denoising with synchrosqueezing

Abstract

Conference

Keywords

ASJC Scopus subject areas

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