Quantitative Tools for Examining the Vocalizations of Juvenile Songbirds
The singing of juvenile songbirds is highly variable and not well stereotyped, a feature that makes it difficult to analyze with existing computational techniques. We present here a method suitable for analyzing such vocalizations, windowed spectral pattern recognition (WSPR). Rather than performing...
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2012-01-01
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Series: | Computational Intelligence and Neuroscience |
Online Access: | http://dx.doi.org/10.1155/2012/261010 |
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doaj-db7bc4b39f6e4d1da6a3cf5d2e7b6a492020-11-24T22:48:18ZengHindawi LimitedComputational Intelligence and Neuroscience1687-52651687-52732012-01-01201210.1155/2012/261010261010Quantitative Tools for Examining the Vocalizations of Juvenile SongbirdsCameron D. Wellock0George N. Reeke1Laboratory of Biological Modeling, The Rockefeller University, 1230 York Avenue, New York, NY 10065, USALaboratory of Biological Modeling, The Rockefeller University, 1230 York Avenue, New York, NY 10065, USAThe singing of juvenile songbirds is highly variable and not well stereotyped, a feature that makes it difficult to analyze with existing computational techniques. We present here a method suitable for analyzing such vocalizations, windowed spectral pattern recognition (WSPR). Rather than performing pairwise sample comparisons, WSPR measures the typicality of a sample against a large sample set. We also illustrate how WSPR can be used to perform a variety of tasks, such as sample classification, song ontogeny measurement, and song variability measurement. Finally, we present a novel measure, based on WSPR, for quantifying the apparent complexity of a bird’s singing.http://dx.doi.org/10.1155/2012/261010 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Cameron D. Wellock George N. Reeke |
spellingShingle |
Cameron D. Wellock George N. Reeke Quantitative Tools for Examining the Vocalizations of Juvenile Songbirds Computational Intelligence and Neuroscience |
author_facet |
Cameron D. Wellock George N. Reeke |
author_sort |
Cameron D. Wellock |
title |
Quantitative Tools for Examining the Vocalizations of Juvenile Songbirds |
title_short |
Quantitative Tools for Examining the Vocalizations of Juvenile Songbirds |
title_full |
Quantitative Tools for Examining the Vocalizations of Juvenile Songbirds |
title_fullStr |
Quantitative Tools for Examining the Vocalizations of Juvenile Songbirds |
title_full_unstemmed |
Quantitative Tools for Examining the Vocalizations of Juvenile Songbirds |
title_sort |
quantitative tools for examining the vocalizations of juvenile songbirds |
publisher |
Hindawi Limited |
series |
Computational Intelligence and Neuroscience |
issn |
1687-5265 1687-5273 |
publishDate |
2012-01-01 |
description |
The singing of juvenile songbirds is highly variable and not well stereotyped, a feature that makes it difficult to analyze with existing computational techniques. We present here a method suitable for analyzing such vocalizations, windowed spectral pattern recognition (WSPR). Rather than performing pairwise sample comparisons, WSPR measures the typicality of a sample against a large sample set. We also illustrate how WSPR can be used to perform a variety of tasks, such as sample classification, song ontogeny measurement, and song variability measurement. Finally, we present a novel measure, based on WSPR, for quantifying the apparent complexity of a bird’s singing. |
url |
http://dx.doi.org/10.1155/2012/261010 |
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