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dc.contributor.advisor Wörgötter, Florentin Prof. Dr.
dc.contributor.author Noriega Romero Vargas, Maria Florencia
dc.date.accessioned 2018-08-06T08:46:02Z
dc.date.available 2018-08-06T08:46:02Z
dc.date.issued 2018-08-06
dc.identifier.uri http://hdl.handle.net/11858/00-1735-0000-002E-E469-8
dc.language.iso eng de
dc.relation.uri http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.ddc 570 de
dc.title Revealing structure in vocalisations of parrots and social whales de
dc.type doctoralThesis de
dc.contributor.referee Timme, Marc Prof. Dr.
dc.date.examination 2017-08-07
dc.description.abstracteng This thesis proposes methods to investigate structure in bioacoustic signals. For this two frameworks are proposed. The first concerns the automatic annotation of audio recordings by using supervised machine learning methods. The second concerns a quantitative analysis of temporal and combinatorial patterns in vocal sequences of animals by using non-parametric statistics. These methods are used to investigate vocalisations of two wild living animals — known very little — in their natural ecosystems: lilac crowned parrots and pilot whales. de
dc.contributor.coReferee Hammerschmidt, Kurt Dr.
dc.subject.eng bioacoustics de
dc.subject.eng pilot whales de
dc.subject.eng parrots de
dc.subject.eng audio signal processing de
dc.subject.eng machine learning de
dc.subject.eng animal vocalisations de
dc.identifier.urn urn:nbn:de:gbv:7-11858/00-1735-0000-002E-E469-8-7
dc.affiliation.institute Göttinger Graduiertenschule für Neurowissenschaften, Biophysik und molekulare Biowissenschaften (GGNB) de
dc.subject.gokfull Biologie (PPN619462639) de
dc.identifier.ppn 1028372825

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