@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",
}