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Classification of bird song syllables using singular vectors of the multitaper spectrogram

Sandsten, Maria LU (2015) 23rd European Signal Processing Conference, 2015 In Signal Processing Conference (EUSIPCO), 2015 23rd European p.554-558
Abstract
Classification of song similarities and differences in one bird species is a subtle problem where the actual answer is more or less unknown. In this paper, the singular vectors when decomposing the multitaper spectrogram are proposed to be used as feature vectors for classification. The advantage is especially for signals consisting of several components which have stochastic variations in the amplitudes as well as the time- and frequency locations. The approach is evaluated and compared to other methods for simulated data and bird song syllables recorded from the great reed warbler. The results show that in classification where there are strong similar components in all the signals but where the structure of weaker components are... (More)
Classification of song similarities and differences in one bird species is a subtle problem where the actual answer is more or less unknown. In this paper, the singular vectors when decomposing the multitaper spectrogram are proposed to be used as feature vectors for classification. The advantage is especially for signals consisting of several components which have stochastic variations in the amplitudes as well as the time- and frequency locations. The approach is evaluated and compared to other methods for simulated data and bird song syllables recorded from the great reed warbler. The results show that in classification where there are strong similar components in all the signals but where the structure of weaker components are differing between the classes, the singular vectors decomposing the multitaper spectrogram could be useful as features. (Less)
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
in
Signal Processing Conference (EUSIPCO), 2015 23rd European
pages
5 pages
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
23rd European Signal Processing Conference, 2015
external identifiers
  • Scopus:84963979648
DOI
10.1109/EUSIPCO.2015.7362444
language
English
LU publication?
yes
id
7f803d07-ee54-4a8b-97b2-ee845b117c12 (old id 8310638)
date added to LUP
2016-02-26 16:32:48
date last changed
2016-10-13 04:42:52
@misc{7f803d07-ee54-4a8b-97b2-ee845b117c12,
  abstract     = {Classification of song similarities and differences in one bird species is a subtle problem where the actual answer is more or less unknown. In this paper, the singular vectors when decomposing the multitaper spectrogram are proposed to be used as feature vectors for classification. The advantage is especially for signals consisting of several components which have stochastic variations in the amplitudes as well as the time- and frequency locations. The approach is evaluated and compared to other methods for simulated data and bird song syllables recorded from the great reed warbler. The results show that in classification where there are strong similar components in all the signals but where the structure of weaker components are differing between the classes, the singular vectors decomposing the multitaper spectrogram could be useful as features.},
  author       = {Sandsten, Maria},
  language     = {eng},
  pages        = {554--558},
  publisher    = {ARRAY(0xa632020)},
  series       = {Signal Processing Conference (EUSIPCO), 2015 23rd European},
  title        = {Classification of bird song syllables using singular vectors of the multitaper spectrogram},
  url          = {http://dx.doi.org/10.1109/EUSIPCO.2015.7362444},
  year         = {2015},
}