{"refrec":{"BRefID":365671,"RR":"<b>Hauer, C.; Nöth, E.; Barnhill, A.; Maier, A.; Guthunz, J.; Hofer, H.; Cheng, R.X.; Barth, V.; Bergler, C.</b> (2023). ORCA-SPY enables killer whale sound source simulation, detection, classification and localization using an integrated deep learning-based segmentation. <i>NPG Scientific Reports 13(1)</i>: 11106. <a href=\"https://dx.doi.org/10.1038/s41598-023-38132-7\" target=\"_blank\">https://dx.doi.org/10.1038/s41598-023-38132-7</a>","BEntID":363393,"PublicFlag":1,"CheckedFlag":0,"wosflag":1,"vabbflag":1,"RefStringPartII":". <i>NPG Scientific Reports 13(1)</i>: 11106. <a href=\"https://dx.doi.org/10.1038/s41598-023-38132-7\" target=\"_blank\">https://dx.doi.org/10.1038/s41598-023-38132-7</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":1,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Hauer, C.; Nöth, E.; Barnhill, A.; Maier, A.; Guthunz, J.; Hofer, H.; Cheng, R.X.; Barth, V.; Bergler, C.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Hauer, C. <i>et al.</i>","Englishabstract":"Acoustic identification of vocalizing individuals opens up new and deeper insights into animal communications, such as individual-/group-specific dialects, turn-taking events, and dialogs. However, establishing an association between an individual animal and its emitted signal is usually non-trivial, especially for animals underwater. Consequently, a collection of marine species-, array-, and position-specific ground truth localization data is extremely challenging, which strongly limits possibilities to evaluate localization methods beforehand or at all. This study presents ORCA-SPY, a fully-automated sound source simulation, classification and localization framework for passive killer whale (Orcinus orca) acoustic monitoring that is embedded into PAMGuard, a widely used bioacoustic software toolkit. ORCA-SPY enables array- and position-specific multichannel audio stream generation to simulate real-world ground truth killer whale localization data and provides a hybrid sound source identification approach integrating ANIMAL-SPOT, a state-of-the-art deep learning-based orca detection network, followed by downstream Time-Difference-Of-Arrival localization. ORCA-SPY was evaluated on simulated multichannel underwater audio streams including various killer whale vocalization events within a large-scale experimental setup benefiting from previous real-world fieldwork experience. Across all 58,320 embedded vocalizing killer whale events, subject to various hydrophone array geometries, call types, distances, and noise conditions responsible for a signal-to-noise ratio varying from −14.2 dB to 3 dB, a detection rate of 94.0 % was achieved with an average localization error of 7.01∘. ORCA-SPY was field-tested on Lake Stechlin in Brandenburg Germany under laboratory conditions with a focus on localization. During the field test, 3889 localization events were observed with an average error of 29.19∘ and a median error of 17.54∘. ORCA-SPY was deployed successfully during the DeepAL fieldwork 2022 expedition (DLFW22) in Northern British Columbia, with a mean average error of 20.01∘ and a median error of 11.01∘ across 503 localization events. ORCA-SPY is an open-source and publicly available software framework, which can be adapted to various recording conditions as well as animal species.","AbstractOtherLang":null,"BibLvlCode":"AS","StandardTitle":"ORCA-SPY enables killer whale sound source simulation, detection, classification and localization using an integrated deep learning-based segmentation","OrigTitleLangCode":"en","OrigTitleLangCodeExtended":"eng","OrigTitleLangID":15,"DateLastModified":{"date":"2026-06-13 01:32:00.591376","timezone_type":1,"timezone":"+02:00"},"UserAccessRight":null,"UserAccID":null,"AuthorKeywords":null,"OtherDescriptors":null,"Notes":null,"AnaPub":2023,"MonPub":null,"DateUpdate":"2023-07-10","DateCreate":"2023-07-10","SecASFANote":null,"ConfID":null,"PeerRev":1,"VlizCoreFlag":1,"WoScode":"WOS:001027101700031","VABBcode":null,"OpenAcc":1,"DOI":"10.1038/s41598-023-38132-7"},"refs":null,"anarec":{"AnaID":365671,"PubliDate":2023,"Pagination":"11106","XtraPublOfAnaID":null,"ISBN":null,"Volume":"13","Issue":"1","BRefMon":null,"BRefMonRR":null,"BRefXtra":null,"BRefXtraRR":null,"SerBRefID":208093,"SerRR":"Scientific Reports (Nature Publishing Group). 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