A Learning-based Framework for Line-spectra Super-resolution

Gautier Izacard; Brett Bernstein; Carlos Fernandez-Granda

doi:10.1109/ICASSP.2019.8682882

A Learning-based Framework for Line-spectra Super-resolution

Gautier Izacard, Brett Bernstein, Carlos Fernandez-Granda

Mathematics

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

Abstract

We propose a learning-based approach for estimating the spectrum of a multisinusoidal signal from a finite number of samples. A neural-network is trained to approximate the spectra of such signals on simulated data. The proposed methodology is very flexible: adapting to different signal and noise models only requires modifying the training data accordingly. Numerical experiments show that the approach performs competitively with classical methods designed for additive Gaussian noise at a range of noise levels, and is also effective in the presence of impulsive noise.

Original language	English (US)
Title of host publication	2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3632-3636
Number of pages	5
ISBN (Electronic)	9781479981311
DOIs	https://doi.org/10.1109/ICASSP.2019.8682882
State	Published - May 2019
Event	44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom Duration: May 12 2019 → May 17 2019

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2019-May
ISSN (Print)	1520-6149

Conference

Conference	44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Country	United Kingdom
City	Brighton
Period	5/12/19 → 5/17/19

Fingerprint

Keywords

Line-spectra estimation
deep learning
neural networks
sparse noise
spectral super-resolution

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

Cite this

Izacard, G., Bernstein, B., & Fernandez-Granda, C. (2019). A Learning-based Framework for Line-spectra Super-resolution. In 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings (pp. 3632-3636). [8682882] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2019-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2019.8682882

A Learning-based Framework for Line-spectra Super-resolution. / Izacard, Gautier; Bernstein, Brett; Fernandez-Granda, Carlos.

2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 3632-3636 8682882 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2019-May).

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

Izacard, G, Bernstein, B & Fernandez-Granda, C 2019, A Learning-based Framework for Line-spectra Super-resolution. in 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings., 8682882, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2019-May, Institute of Electrical and Electronics Engineers Inc., pp. 3632-3636, 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019, Brighton, United Kingdom, 5/12/19. https://doi.org/10.1109/ICASSP.2019.8682882

Izacard G, Bernstein B, Fernandez-Granda C. A Learning-based Framework for Line-spectra Super-resolution. In 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 3632-3636. 8682882. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2019.8682882

Izacard, Gautier ; Bernstein, Brett ; Fernandez-Granda, Carlos. / A Learning-based Framework for Line-spectra Super-resolution. 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 3632-3636 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

@inproceedings{19b3f139abe9416f933ce8c639ae9b09,

title = "A Learning-based Framework for Line-spectra Super-resolution",

abstract = "We propose a learning-based approach for estimating the spectrum of a multisinusoidal signal from a finite number of samples. A neural-network is trained to approximate the spectra of such signals on simulated data. The proposed methodology is very flexible: adapting to different signal and noise models only requires modifying the training data accordingly. Numerical experiments show that the approach performs competitively with classical methods designed for additive Gaussian noise at a range of noise levels, and is also effective in the presence of impulsive noise.",

keywords = "Line-spectra estimation, deep learning, neural networks, sparse noise, spectral super-resolution",

author = "Gautier Izacard and Brett Bernstein and Carlos Fernandez-Granda",

year = "2019",

month = may

doi = "10.1109/ICASSP.2019.8682882",

language = "English (US)",

series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "3632--3636",

booktitle = "2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings",

note = "44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 ; Conference date: 12-05-2019 Through 17-05-2019",

}

TY - GEN

T1 - A Learning-based Framework for Line-spectra Super-resolution

AU - Izacard, Gautier

AU - Bernstein, Brett

AU - Fernandez-Granda, Carlos

PY - 2019/5

Y1 - 2019/5

N2 - We propose a learning-based approach for estimating the spectrum of a multisinusoidal signal from a finite number of samples. A neural-network is trained to approximate the spectra of such signals on simulated data. The proposed methodology is very flexible: adapting to different signal and noise models only requires modifying the training data accordingly. Numerical experiments show that the approach performs competitively with classical methods designed for additive Gaussian noise at a range of noise levels, and is also effective in the presence of impulsive noise.

AB - We propose a learning-based approach for estimating the spectrum of a multisinusoidal signal from a finite number of samples. A neural-network is trained to approximate the spectra of such signals on simulated data. The proposed methodology is very flexible: adapting to different signal and noise models only requires modifying the training data accordingly. Numerical experiments show that the approach performs competitively with classical methods designed for additive Gaussian noise at a range of noise levels, and is also effective in the presence of impulsive noise.

KW - Line-spectra estimation

KW - deep learning

KW - neural networks

KW - sparse noise

KW - spectral super-resolution

UR - http://www.scopus.com/inward/record.url?scp=85068972773&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85068972773&partnerID=8YFLogxK

U2 - 10.1109/ICASSP.2019.8682882

DO - 10.1109/ICASSP.2019.8682882

M3 - Conference contribution

AN - SCOPUS:85068972773

T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

SP - 3632

EP - 3636

BT - 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019

Y2 - 12 May 2019 through 17 May 2019

ER -

Access to Document

10.1109/ICASSP.2019.8682882