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. • Framework for classifying lion roaring from acceleration data using a U-Net. • Separate roaring from other behaviours present in the same acceleration sequence. • Detect roaring while the lion is walking. • Detect roaring of males and females.
Rast et al. (Wed,) studied this question.