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Efficient Thomson Spectral Estimator with Time-shifted Windows

Reinhold, Isabella LU and Sandsten, Maria LU (2019) 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 2019-May. p.4983-4987
Abstract

In this paper optimal spectral analysis window shapes, using weighted discrete prolate spheroidal sequences as basis functions, are proposed. These windows are not typically positive or even. The windows are time-shifted, combining the computational efficiency of the Welch method and the appealing property of predefined frequency resolution of the Thomson spectral estimator. The parameters of the optimal windows are found by minimising the resulting spectral covariances and optimising the window overlap, for the predetermined frequency resolution and number of windows. The windows are found to have low side lobes, giving small spectral leakage, and the final spectral estimate gives close to optimal variance reduction, i.e. the... (More)

In this paper optimal spectral analysis window shapes, using weighted discrete prolate spheroidal sequences as basis functions, are proposed. These windows are not typically positive or even. The windows are time-shifted, combining the computational efficiency of the Welch method and the appealing property of predefined frequency resolution of the Thomson spectral estimator. The parameters of the optimal windows are found by minimising the resulting spectral covariances and optimising the window overlap, for the predetermined frequency resolution and number of windows. The windows are found to have low side lobes, giving small spectral leakage, and the final spectral estimate gives close to optimal variance reduction, i.e. the covariance between different sub-spectra is very small.

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author
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organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
DPSS, Slepian functions, spectral leakage, variance, Welch method
host publication
2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
volume
2019-May
article number
8683588
pages
5 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
conference location
Brighton, United Kingdom
conference dates
2019-05-12 - 2019-05-17
external identifiers
  • scopus:85068991965
ISBN
9781479981311
DOI
10.1109/ICASSP.2019.8683588
language
English
LU publication?
yes
id
f116d368-6712-45bd-b77b-40aea151b739
date added to LUP
2019-07-23 17:14:25
date last changed
2022-04-26 03:41:55
@inproceedings{f116d368-6712-45bd-b77b-40aea151b739,
  abstract     = {{<p>In this paper optimal spectral analysis window shapes, using weighted discrete prolate spheroidal sequences as basis functions, are proposed. These windows are not typically positive or even. The windows are time-shifted, combining the computational efficiency of the Welch method and the appealing property of predefined frequency resolution of the Thomson spectral estimator. The parameters of the optimal windows are found by minimising the resulting spectral covariances and optimising the window overlap, for the predetermined frequency resolution and number of windows. The windows are found to have low side lobes, giving small spectral leakage, and the final spectral estimate gives close to optimal variance reduction, i.e. the covariance between different sub-spectra is very small.</p>}},
  author       = {{Reinhold, Isabella and Sandsten, Maria}},
  booktitle    = {{2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings}},
  isbn         = {{9781479981311}},
  keywords     = {{DPSS; Slepian functions; spectral leakage; variance; Welch method}},
  language     = {{eng}},
  pages        = {{4983--4987}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Efficient Thomson Spectral Estimator with Time-shifted Windows}},
  url          = {{http://dx.doi.org/10.1109/ICASSP.2019.8683588}},
  doi          = {{10.1109/ICASSP.2019.8683588}},
  volume       = {{2019-May}},
  year         = {{2019}},
}