Towards automatic detection and classification of orca (Orcinus orca) calls using cross-correlation methods

Orcas (Orcinus orca) are known for complex vocalization. Their social structure consists of pods with unique call repertoires and clans sharing vocal traditions. Call repertoires are typically established visually and aurally and are used for pod identification. Automatic tools are, however, more suited for large data sets. An Icelandic orca pod occurring in 2019 in the Ligurian Sea provided a unique occasion for collecting recordings of an isolated pod in natural conditions. Recordings were analyzed visually and aurally to create a pod catalog. The R package "warbleR" was used for the first time on a small subsample of orca data to automatically detect sound samples and classify sound types. We found cross-correlation methods with the Pearson correlation coefficient (PCC) to successfully classify sound types, though false positives occur. We compared our catalog to Icelandic and Antarctic ones checking for similarities and dissimilarities. We found five matches in the Icelandic catalog, two of which had high PCCs ranges (0.62-0.67; 0.60-0.65). Our automatic approach was limited by background noise and variability of orca vocalizations, and it was computationally demanding. We show cross-correlation methods with the PCC can be a powerful tool to verify audio-visual repertoire matches between orca from different regions.