| 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.identifier.uri | http://dx.doi.org/10.53846/goediss-6992 | |
| 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 | |