dc.contributor.author | Stowell, D | |
dc.contributor.author | Petrusková, T | |
dc.contributor.author | Šálek, M | |
dc.contributor.author | Linhart, P | |
dc.date.accessioned | 2019-05-30T10:55:52Z | |
dc.date.available | 2019-03-21 | |
dc.date.available | 2019-05-30T10:55:52Z | |
dc.date.issued | 2019-04-10 | |
dc.identifier.citation | Stowell, D., Petrusková, T., Šálek, M. and Linhart, P. (2019). Automatic acoustic identification of individuals in multiple species: improving identification across recording conditions. Journal of The Royal Society Interface, [online] 16(153), p.20180940. Available at: https://royalsocietypublishing.org/doi/10.1098/rsif.2018.0940 [Accessed 30 May 2019]. | en_US |
dc.identifier.issn | 1742-5689 | |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/57785 | |
dc.description.abstract | Many animals emit vocal sounds which, independently from the sounds' function, embed some individually-distinctive signature. Thus the automatic recognition of individuals by sound is a potentially powerful tool for zoology and ecology research and practical monitoring. Here we present a general automatic identification method, that can work across multiple animal species with various levels of complexity in their communication systems. We further introduce new analysis techniques based on dataset manipulations that can evaluate the robustness and generality of a classifier. By using these techniques we confirmed the presence of experimental confounds in situations resembling those from past studies. We introduce data manipulations that can reduce the impact of these confounds, compatible with any classifier. We suggest that assessment of confounds should become a standard part of future studies to ensure they do not report over-optimistic results. We provide annotated recordings used for analyses along with this study and we call for dataset sharing to be a common practice to enhance development of methods and comparisons of results. | en_US |
dc.publisher | Royal Society, The | en_US |
dc.relation.ispartof | Journal of the Royal Society Interface | |
dc.relation.isreplacedby | 123456789/62376 | |
dc.relation.isreplacedby | https://qmro.qmul.ac.uk/xmlui/handle/123456789/62376 | |
dc.rights | Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. | |
dc.rights | Attribution 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/us/ | * |
dc.title | Automatic acoustic identification of individual animals: Improving generalisation across species and recording conditions | en_US |
dc.type | Article | en_US |
dc.rights.holder | © 2019 The Authors. | |
dc.identifier.doi | 10.1098/rsif.2018.0940 | |
pubs.author-url | http://arxiv.org/abs/1810.09273v1 | en_US |
pubs.issue | 153 | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Published | en_US |
pubs.volume | 16 | en_US |
dcterms.dateAccepted | 2019-03-21 | |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |
qmul.funder | Structured machine listening for soundscapes with multiple birds::EPSRC | en_US |