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The encoding of individual identity in dolphin signature whistles : how much information is needed?
Item metadata
dc.contributor.author | Kershenbaum, Arik | |
dc.contributor.author | Sayigh, Laela | |
dc.contributor.author | Janik, Vincent M. | |
dc.date.accessioned | 2013-11-04T11:01:10Z | |
dc.date.available | 2013-11-04T11:01:10Z | |
dc.date.issued | 2013-10-23 | |
dc.identifier.citation | Kershenbaum , A , Sayigh , L & Janik , V M 2013 , ' The encoding of individual identity in dolphin signature whistles : how much information is needed? ' , PLoS One , vol. 8 , no. 10 , e77671 . https://doi.org/10.1371/journal.pone.0077671 | en |
dc.identifier.issn | 1932-6203 | |
dc.identifier.other | PURE: 69489277 | |
dc.identifier.other | PURE UUID: b7dedffb-53d3-44e9-a74d-591f71857654 | |
dc.identifier.other | Scopus: 84885968521 | |
dc.identifier.other | ORCID: /0000-0001-7894-0121/work/60427857 | |
dc.identifier.uri | https://hdl.handle.net/10023/4142 | |
dc.description.abstract | Bottlenose dolphins (Tursiops truncatus) produce many vocalisations, including whistles that are unique to the individual producing them. Such “signature whistles” play a role in individual recognition and maintaining group integrity. Previous work has shown that humans can successfully group the spectrographic representations of signature whistles according to the individual dolphins that produced them. However, attempts at using mathematical algorithms to perform a similar task have been less successful. A greater understanding of the encoding of identity information in signature whistles is important for assessing similarity of whistles and thus social influences on the development of these learned calls. We re-examined 400 signature whistles from 20 individual dolphins used in a previous study, and tested the performance of new mathematical algorithms. We compared the measure used in the original study (correlation matrix of evenly sampled frequency measurements) to one used in several previous studies (similarity matrix of time-warped whistles), and to a new algorithm based on the Parsons code, used in music retrieval databases. The Parsons code records the direction of frequency change at each time step, and is effective at capturing human perception of music. We analysed similarity matrices from each of these three techniques, as well as a random control, by unsupervised clustering using three separate techniques: k-means clustering, hierarchical clustering, and an adaptive resonance theory neural network. For each of the three clustering techniques, a seven-level Parsons algorithm provided better clustering than the correlation and dynamic time warping algorithms, and was closer to the near-perfect visual categorisations of human judges. Thus, the Parsons code captures much of the individual identity information present in signature whistles, and may prove useful in studies requiring quantification of whistle similarity. | |
dc.language.iso | eng | |
dc.relation.ispartof | PLoS One | en |
dc.rights | © 2013 Kershenbaum et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | en |
dc.subject | Bottlenose dolphin | en |
dc.subject | Signature whistles | en |
dc.subject | Individual recognition | en |
dc.subject | Identity information | en |
dc.subject | Social influences | en |
dc.subject | Parsons code | en |
dc.subject | Frequency measurements | en |
dc.subject | Clustering algorithms | en |
dc.subject | Q Science | en |
dc.subject.lcc | Q | en |
dc.title | The encoding of individual identity in dolphin signature whistles : how much information is needed? | en |
dc.type | Journal article | en |
dc.description.version | Publisher PDF | en |
dc.contributor.institution | University of St Andrews. School of Biology | en |
dc.contributor.institution | University of St Andrews. Sea Mammal Research Unit | en |
dc.contributor.institution | University of St Andrews. Marine Alliance for Science & Technology Scotland | en |
dc.contributor.institution | University of St Andrews. Scottish Oceans Institute | en |
dc.contributor.institution | University of St Andrews. Institute of Behavioural and Neural Sciences | en |
dc.contributor.institution | University of St Andrews. Centre for Social Learning & Cognitive Evolution | en |
dc.contributor.institution | University of St Andrews. Bioacoustics group | en |
dc.identifier.doi | https://doi.org/10.1371/journal.pone.0077671 | |
dc.description.status | Peer reviewed | en |
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