Did u hear that? Working with mixed behaviours when classifying animal behaviour from acceleration data using a U-Net

Rast, Wanja (avtor), Götz, Theresa (avtor), Cloete, Claudine (avtor), Berger, Anne (avtor), Chamaillé-Jammes, Simon (avtor), Krofel, Miha (avtor), Portas, Ruben (avtor), Aschenborn, Ortwin (avtor), Melzheimer, Joerg (avtor)

Current approaches to detect roaring behaviour of lions (Panthera leo) within acceleration data are scarce and limited to males that are stationary. We propose a segmentation approach using a Fully Convolutional Neural Network (U-Net) that can also work with data from females and roars that happen while the lion is walking. We equipped seven lions with a GPS/accelerometer and an audio logger. We merged audio and acceleration data to identify roaring signals in the acceleration data. We then trained a U-Net to differentiate between ''roaring'' and ''no roaring'' data. As training data we used segments containing only ''no roaring'' data and segments with a mix of ''roaring'' and ''no roaring''. Finally, we used our classifier on the complete data from five lions to test how well the classifier would perform when roaring events only make up a small portion of the data. Our model shows a precision for ''roaring'' data points of 0.94±0.01 (mean ±95% confidence interval) and a recall of 0.73 ± 0.01 over all individuals. Based on this, we could correctly identify between 90% and 96% of all roaring bouts with a precision between 87% and 100%. We showed that our approach can detect lion roaring behaviour of males and females in a stationary and walking context reliably. This can broaden the context in which lion communication can be studied and thus provide less biased data.

• Jezik: Angleški jezik
• Ključne besede: mixed behaviour classification, Panthera leo, u-net, vocalisation
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: BF - Biotehniška fakulteta
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2026
• Št. strani: 10 str.
• Številčenje: Vol. 95, art. 103761
• PID: 20.500.12556/RUL-182382
• UDK: 591.582:599.742.711.1
• ISSN pri članku: 1878-0512
• DOI: 10.1016/j.ecoinf.2026.103761
• COBISS.SI-ID: 275915011
• Datum objave v RUL: 08.05.2026

Gradivo je del revije

• Naslov: Ecological informatics
• Založnik: Elsevier B.V.
• ISSN: 1878-0512
• COBISS.SI-ID: 62725635

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: klasifikacija vedenj, levi, u-net, merilnik pospeškov, oglašanje

Projekti

• Financer: ARIS - Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije
• Številka projekta: P4-0059
• Naslov: Gozd, gozdarstvo in obnovljivi gozdni viri

• Financer: ARIS - Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije
• Številka projekta: J1-50013
• Naslov: ExtremePredator: Odkrivanje ekološke vloge vrhovnih plenilcev v ekstremnih okoljih