@inproceedings{d0e09576b6e649bbbe30d1c27ab83c0a,
title = "Jazz: A companion to music for frequency estimation with missing data",
abstract = "Frequency estimation is a classical problem in signal processing, with applications ranging from sensor array processing to wireless communications and structural health monitoring. Modern algorithms based on atomic norm minimization can cope with missing data but incur a high computational cost. To recover missing data from an ensemble of frequency-sparse signals, we propose a computationally efficient low-rank tensor completion algorithm that exploits the fact that each signal in the ensemble can be associated with a Toeplitz matrix. We name our algorithm JAZZ in the spirit of the classical MUSIC algorithm for frequency estimation and in tribute to the random, improvisational nature of jazz music.",
keywords = "JAZZ, MUSIC, Toeplitz matrices, array processing, low-rank, structural health monitoring, tensors",
author = "Qiuwei Li and Shuang Li and Hassan Mansour and Wakin, {Michael B.} and Dehui Yang and Zhihui Zhu",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 ; Conference date: 05-03-2017 Through 09-03-2017",
year = "2017",
month = jun,
day = "16",
doi = "10.1109/ICASSP.2017.7952754",
language = "English",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3236--3240",
booktitle = "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings",
}