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Classification of bird song syllables using wigner-ville ambiguity function cross-terms

Sandsten, Maria LU and Brynolfsson, Johan LU (2017) 25th European Signal Processing Conference, EUSIPCO 2017 2017-January. p.1739-1743
Abstract

A novel feature extraction method for lowdimensional signal representation is presented. The features are useful for classification of non-stationary multi-component signals with stochastic variation in amplitudes and time-frequency locations. Using a penalty function to suppress the Wigner-Ville ambiguity function auto-terms, the proposed feature set is based on the cross-term doppler- and lag profiles. The investigation considers classification where strong similar components appear in all signals and where the differences between classes are related to weaker components. The approach is evaluated and compared with established methods for simulated data and bird song syllables of the great reed warbler. The results show that the novel... (More)

A novel feature extraction method for lowdimensional signal representation is presented. The features are useful for classification of non-stationary multi-component signals with stochastic variation in amplitudes and time-frequency locations. Using a penalty function to suppress the Wigner-Ville ambiguity function auto-terms, the proposed feature set is based on the cross-term doppler- and lag profiles. The investigation considers classification where strong similar components appear in all signals and where the differences between classes are related to weaker components. The approach is evaluated and compared with established methods for simulated data and bird song syllables of the great reed warbler. The results show that the novel feature extraction method gives a better classification than established methods used in bird song analysis.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
25th European Signal Processing Conference, EUSIPCO 2017
volume
2017-January
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:85041483267
ISBN
9780992862671
DOI
10.23919/EUSIPCO.2017.8081507
language
English
LU publication?
yes
id
098597ee-2357-4709-b07e-926677dd921c
date added to LUP
2018-02-22 11:52:15
date last changed
2019-07-16 03:44:03
@inproceedings{098597ee-2357-4709-b07e-926677dd921c,
  abstract     = {<p>A novel feature extraction method for lowdimensional signal representation is presented. The features are useful for classification of non-stationary multi-component signals with stochastic variation in amplitudes and time-frequency locations. Using a penalty function to suppress the Wigner-Ville ambiguity function auto-terms, the proposed feature set is based on the cross-term doppler- and lag profiles. The investigation considers classification where strong similar components appear in all signals and where the differences between classes are related to weaker components. The approach is evaluated and compared with established methods for simulated data and bird song syllables of the great reed warbler. The results show that the novel feature extraction method gives a better classification than established methods used in bird song analysis.</p>},
  author       = {Sandsten, Maria and Brynolfsson, Johan},
  isbn         = {9780992862671},
  language     = {eng},
  location     = {Kos, Greece},
  month        = {10},
  pages        = {1739--1743},
  publisher    = {Institute of Electrical and Electronics Engineers Inc.},
  title        = {Classification of bird song syllables using wigner-ville ambiguity function cross-terms},
  url          = {http://dx.doi.org/10.23919/EUSIPCO.2017.8081507},
  volume       = {2017-January},
  year         = {2017},
}