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dc.contributor.authorShamir, L.
dc.contributor.authorYerby, C.
dc.contributor.authorSimpson, R.
dc.contributor.authorVon Benda-Beckmann, A.M.
dc.contributor.authorTyack, P.
dc.contributor.authorSamarra, F.
dc.contributor.authorMiller, P.
dc.contributor.authorWallin, J.
dc.identifier.citationShamir , L , Yerby , C , Simpson , R , Von Benda-Beckmann , A M , Tyack , P , Samarra , F , Miller , P & Wallin , J 2014 , ' Classification of large acoustic datasets using machine learning and crowdsourcing : application to whale calls ' , Journal of the Acoustical Society of America , vol. 135 , no. 2 , pp. 953-962 .
dc.identifier.otherPURE: 145950020
dc.identifier.otherPURE UUID: 794df8db-9371-4452-9e8a-8b386eb85e55
dc.identifier.otherScopus: 84900602686
dc.identifier.otherWOS: 000331846000048
dc.identifier.otherORCID: /0000-0002-8409-4790/work/60887894
dc.descriptionA.M.v.B.B. acknowledges support from the Sea Mammal Research Unit at the University of St. Andrews (Professor Ian Boyd) and Woods Hole Oceanographic Institution for the Whale FM project. P.T. received funding from the Marine Alliance for Science and Technology for Scotland (MASTS).en
dc.description.abstractVocal communication is a primary communication method of killer and pilot whales, and is used for transmitting a broad range of messages and information for short and long distance. The large variation in call types of these species makes it challenging to categorize them. In this study, sounds recorded by audio sensors carried by ten killer whales and eight pilot whales close to the coasts of Norway, Iceland, and the Bahamas were analyzed using computer methods and citizen scientists as part of the Whale FM project. Results show that the computer analysis automatically separated the killer whales into Icelandic and Norwegian whales, and the pilot whales were separated into Norwegian long-finned and Bahamas short-finned pilot whales, showing that at least some whales from these two locations have different acoustic repertoires that can be sensed by the computer analysis. The citizen science analysis was also able to separate the whales to locations by their sounds, but the separation was somewhat less accurate compared to the computer method.
dc.relation.ispartofJournal of the Acoustical Society of Americaen
dc.rightsCopyright 2014 Acoustical Society of America. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the Acoustical Society of America. The following article appeared in Journal of the Acoustical Society of America and may be found at
dc.subjectQH301 Biologyen
dc.titleClassification of large acoustic datasets using machine learning and crowdsourcing : application to whale callsen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Biologyen
dc.contributor.institutionUniversity of St Andrews. Marine Alliance for Science & Technology Scotlanden
dc.contributor.institutionUniversity of St Andrews. Sea Mammal Research Uniten
dc.contributor.institutionUniversity of St Andrews. Sound Tags Groupen
dc.contributor.institutionUniversity of St Andrews. Bioacoustics groupen
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.description.statusPeer revieweden

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