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Classification of one-dimensional non-stationary signals using the Wigner-Ville distribution in convolutional neural networks

Brynolfsson, Johan LU and Sandsten, Maria LU (2017) 25th European Signal Processing Conference, EUSIPCO 2017 p.326-330
Abstract

In this paper we argue that the Wigner-Ville distribution (WVD), instead of the spectrogram, should be used as basic input into convolutional neural network (CNN) based classification schemes. The WVD has superior resolution and localization as compared to other time-frequency representations. We present a method where a large-size kernel may be learned from the data, to enhance features important for classification. We back up our claims with theory, as well as application on simulated examples and show superior performance as compared to the commonly used spectrogram.

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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
25th European Signal Processing Conference, EUSIPCO 2017
pages
5 pages
publisher
Institute of Electrical and Electronics Engineers Inc.
conference name
25th European Signal Processing Conference, EUSIPCO 2017
conference location
Kos, Greece
conference dates
2017-08-28 - 2017-09-02
external identifiers
  • scopus:85041504101
ISBN
9780992862671
DOI
10.23919/EUSIPCO.2017.8081222
language
English
LU publication?
yes
id
e6f085a6-088f-493d-a25b-6eeebf89ab2c
date added to LUP
2018-02-22 11:49:26
date last changed
2019-07-16 03:44:04
@inproceedings{e6f085a6-088f-493d-a25b-6eeebf89ab2c,
  abstract     = {<p>In this paper we argue that the Wigner-Ville distribution (WVD), instead of the spectrogram, should be used as basic input into convolutional neural network (CNN) based classification schemes. The WVD has superior resolution and localization as compared to other time-frequency representations. We present a method where a large-size kernel may be learned from the data, to enhance features important for classification. We back up our claims with theory, as well as application on simulated examples and show superior performance as compared to the commonly used spectrogram.</p>},
  author       = {Brynolfsson, Johan and Sandsten, Maria},
  isbn         = {9780992862671},
  language     = {eng},
  location     = {Kos, Greece},
  month        = {10},
  pages        = {326--330},
  publisher    = {Institute of Electrical and Electronics Engineers Inc.},
  title        = {Classification of one-dimensional non-stationary signals using the Wigner-Ville distribution in convolutional neural networks},
  url          = {http://dx.doi.org/10.23919/EUSIPCO.2017.8081222},
  year         = {2017},
}