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dc.contributor.authorAllen, Jenny A.
dc.contributor.authorMurray, Anita
dc.contributor.authorNoad, Michael J.
dc.contributor.authorDunlop, Rebecca A.
dc.contributor.authorGarland, Ellen Clare
dc.identifier.citationAllen , J A , Murray , A , Noad , M J , Dunlop , R A & Garland , E C 2017 , ' Using self-organizing maps to classify humpback whale song units and quantify their similarity ' , Journal of the Acoustical Society of America , vol. 142 , no. 4 , pp. 1943-1952 .
dc.identifier.otherPURE: 249711869
dc.identifier.otherPURE UUID: 23c48721-dfaf-4525-88e9-4ac2d22e0631
dc.identifier.otherScopus: 85031302820
dc.identifier.otherORCID: /0000-0002-8240-1267/work/49580220
dc.identifier.otherWOS: 000413528900036
dc.descriptionE.C.G. was funded by a Royal Society Newton International Fellowship.en
dc.description.abstractClassification of vocal signals can be undertaken using a wide variety of qualitative and quantitative techniques. Using east Australian humpback whale song from 2002-2014, a subset of vocal signals were acoustically measured and then classified using a self-organizing map (SOM). The SOM created 1) an acoustic dictionary of units representing the song’s repertoire, and 2) Cartesian distance measurements among all unit types (SOM nodes). Utilizing the SOM dictionary as a guide, additional song recordings from east Australia were rapidly (manually) transcribed. To assess the similarity in song sequences, the Cartesian distance output from the SOM was applied in Levenshtein distance similarity analyses as a weighting factor to better incorporate unit similarity in the calculation (previously a qualitative process). SOMs provide a more robust and repeatable means of categorizing acoustic signals along with a clear quantitative measurement of sound type similarity based on acoustic features. This method can be utilized for a wide variety of acoustic databases especially those containing very large datasets, and be applied across the vocalization research community to help address concerns surrounding inconsistency in manual classification.
dc.relation.ispartofJournal of the Acoustical Society of Americaen
dc.rights© 2017, Acoustical Society of America. This work has been made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at
dc.subjectAnimal communicationen
dc.subjectSequence analysisen
dc.subjectNeural networksen
dc.subjectHumpback whaleen
dc.subjectGC Oceanographyen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectQH301 Biologyen
dc.titleUsing self-organizing maps to classify humpback whale song units and quantify their similarityen
dc.typeJournal articleen
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.Centre for Social Learning & Cognitive Evolutionen
dc.contributor.institutionUniversity of St Andrews.Centre for Biological Diversityen
dc.description.statusPeer revieweden

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