20.500.12556/RUL-182382 Did u hear that? Working with mixed behaviours when classifying animal behaviour from acceleration data using a U-Net 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. mixed behaviour classification Panthera leo u-net vocalisation klasifikacija vedenj levi u-net merilnik pospeškov oglašanje true false true Angleški jezik Slovenski jezik Članek v reviji 2026-05-08 13:56:15 2026-05-08 13:56:16 2026-05-09 04:44:21 0000-00-00 00:00:00 2026 0 0 10 str. art. 103761 Vol. 95 2026 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 591.582:599.742.711.1 1878-0512 10.1016/j.ecoinf.2026.103761 275915011 RAZ_Rast_Wanja_2026.pdf RAZ_Rast_Wanja_2026.pdf 1 7BD21D61C2E4DBA99228DAC90F59FF86 0fb5920d0682dfb22a5ee45abe5d79fc28124f7d4a78737a5a9d4884069e64ac 0e24db97-4ad5-11f1-b0ab-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=233579 https://www.sciencedirect.com/science/article/pii/S1574954126001676 1 a08cc9c8-4ad4-11f1-b0ab-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=233578 Biotehniška fakulteta 0 0 0