Unsupervised discrimination of male Tawny owls (Strix aluco) individual calls using robust measurements of the acoustic signal

Vocal individuality has been widely documented in the Tawny owl (Strix aluco); however, all statistical tools employed thus far to discriminate individual vocalisations have relied on prior knowledge regarding number and identity of individuals. In this study, we tested the effectiveness of four unsupervised clustering algorithms in distinguishing among eight Tawny owl males, solely based on acoustic characteristics of their vocalisations. We also employed both traditional bound-based and robust measurements of acoustic signal to compare their efficacy. We finally evaluated the applicability of this method in identifying the number and distribution of the remaining males recorded in our study area. Three of the four unsupervised techniques had a high rate of success in discriminating among vocalisations of the eight males. In all cases, the best results were obtained using robust measurements. However, when extending the analysis to the remaining unknown males recorded, the highest rate of misclassification errors made results more difficult to interpret. Our study provided a useful tool to discriminate male Tawny owls when only their call recordings are available. Furthermore, this method could be extended to other nocturnal and vociferous species, representing one of the few existing approaches for unsupervised classification of individuals based on acoustic features.