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dc.contributor.authorStowell, Den_US
dc.contributor.authorWood, MDen_US
dc.contributor.authorPamuła, Hen_US
dc.contributor.authorStylianou, Yen_US
dc.contributor.authorGlotin, Hen_US
dc.date.accessioned2018-12-04T11:23:35Z
dc.date.available2018-09-20en_US
dc.date.issued2018-11-03en_US
dc.date.submitted2018-11-26T16:26:33.714Z
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/53489
dc.description.abstractAssessing the presence and abundance of birds is important for monitoring specific species as well as overall ecosystem health. Many birds are most readily detected by their sounds, and thus, passive acoustic monitoring is highly appropriate. Yet acoustic monitoring is often held back by practical limitations such as the need for manual configuration, reliance on example sound libraries, low accuracy, low robustness, and limited ability to generalise to novel acoustic conditions. Here, we report outcomes from a collaborative data challenge. We present new acoustic monitoring datasets, summarise the machine learning techniques proposed by challenge teams, conduct detailed performance evaluation, and discuss how such approaches to detection can be integrated into remote monitoring projects. Multiple methods were able to attain performance of around 88% area under the receiver operating characteristic (ROC) curve (AUC), much higher performance than previous general-purpose methods. With modern machine learning, including deep learning, general-purpose acoustic bird detection can achieve very high retrieval rates in remote monitoring data, with no manual recalibration, and no pretraining of the detector for the target species or the acoustic conditions in the target environment.en_US
dc.relation.ispartofMethods in Ecology and Evolutionen_US
dc.rightsThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
dc.titleAutomatic acoustic detection of birds through deep learning: The first Bird Audio Detection challengeen_US
dc.typeArticle
dc.rights.holder© 2018 The Authors.
dc.identifier.doi10.1111/2041-210X.13103en_US
pubs.notesNo embargoen_US
pubs.publication-statusPublisheden_US
dcterms.dateAccepted2018-09-20en_US
qmul.funderStructured machine listening for soundscapes with multiple birds::EPSRCen_US


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