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dc.contributor.authorKershenbaum, Arik
dc.contributor.authorSayigh, Laela
dc.contributor.authorJanik, Vincent M.
dc.date.accessioned2013-11-04T11:01:10Z
dc.date.available2013-11-04T11:01:10Z
dc.date.issued2013-10-23
dc.identifier.citationKershenbaum , 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.0077671en
dc.identifier.issn1932-6203
dc.identifier.otherPURE: 69489277
dc.identifier.otherPURE UUID: b7dedffb-53d3-44e9-a74d-591f71857654
dc.identifier.otherScopus: 84885968521
dc.identifier.otherORCID: /0000-0001-7894-0121/work/60427857
dc.identifier.urihttps://hdl.handle.net/10023/4142
dc.description.abstractBottlenose 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.isoeng
dc.relation.ispartofPLoS Oneen
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.subjectBottlenose dolphinen
dc.subjectSignature whistlesen
dc.subjectIndividual recognitionen
dc.subjectIdentity informationen
dc.subjectSocial influencesen
dc.subjectParsons codeen
dc.subjectFrequency measurementsen
dc.subjectClustering algorithmsen
dc.subjectQ Scienceen
dc.subject.lccQen
dc.titleThe encoding of individual identity in dolphin signature whistles : how much information is needed?en
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Biologyen
dc.contributor.institutionUniversity of St Andrews. Sea Mammal Research Uniten
dc.contributor.institutionUniversity of St Andrews. Marine Alliance for Science & Technology Scotlanden
dc.contributor.institutionUniversity of St Andrews. Scottish Oceans Instituteen
dc.contributor.institutionUniversity of St Andrews. Institute of Behavioural and Neural Sciencesen
dc.contributor.institutionUniversity of St Andrews. Centre for Social Learning & Cognitive Evolutionen
dc.contributor.institutionUniversity of St Andrews. Bioacoustics groupen
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0077671
dc.description.statusPeer revieweden


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