Scientists discover the unique signature of a lion’s roar using machine learning
Univ. of Oxford

With the data collected by the biologgers, the scientists trained a pattern recognition algorithm to “learn” each individual’s roars and then tested the algorithm on sequences that it had not seen before to determine whether the shape of the contour as a whole is an important distinguishing feature.

Results, published in Bioacoustics, reveal that it is possible to classify roars according to individual identity with 91.5% accuracy. These findings suggest that the overall shape of the fundamental frequency (f0) of the full-throated roar contour is consistent within each individuals’ roars and sufficiently different from other individuals to allow for accurate classification of individual identity.

Previous research has shown that lions can recognise the calls of other individuals, allowing them to locate distant companions and also to avoid potentially hostile neighbours. But little has been understood about how individuals convey identity information in the structure of their calls.

These new findings reveal a possible mechanism for individual vocal recognition amongst African lions. They indicate that individual lions may be able to learn the subtle variations in the fundamental frequency of other lions’ roars and thereby associate particular variations with particular identities.