Classification of bird song syllables using singular vectors of the multitaper spectrogram
(2015) 23rd European Signal Processing Conference, 2015 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:
https://lup.lub.lu.se/record/8310638
- author
- Sandsten, Maria LU
- organization
- publishing date
- 2015
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 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
- conference location
- Nice, France
- conference dates
- 2015-08-31 - 2015-09-04
- 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
- 2016-04-04 10:55:53
- date last changed
- 2022-03-15 22:27:00
@inproceedings{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}},
booktitle = {{Signal Processing Conference (EUSIPCO), 2015 23rd European}},
language = {{eng}},
pages = {{554--558}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Classification of bird song syllables using singular vectors of the multitaper spectrogram}},
url = {{http://dx.doi.org/10.1109/EUSIPCO.2015.7362444}},
doi = {{10.1109/EUSIPCO.2015.7362444}},
year = {{2015}},
}