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Orca (Orcinus orca) is known for complex vocalisation. Their social structureconsists of pods and clans sharing unique dialects due to geographic isolation.Sound type repertoires are fundamental for monitoring orca populations and aretypically created visually and aurally. An orca pod occurring in the LigurianSea (Pelagos Sanctuary) in December 2019 provided a unique occasion forlong-term recordings. The numerous data collected with the bottom recorder wereanalysed with a traditional human-driven inspection to create a repertoire ofthis pod and to compare it to catalogues from different orca populations(Icelandic and Antarctic) investigating its origins. Automatic signal detectionand cross-correlation methods (R package warbleR) were used for the first timein orca studies. We found the Pearson cross-correlation method to be efficientfor most pairwise calculations (> 85%) but with false positives. One sound typefrom our repertoire presented a high positive match (range 0.62-0.67) with onefrom the Icelandic catalogue, which was confirmed visually and aurally. Ourfirst attempt to automatically classify orca sound types presented limitationsdue to background noise and sound complexity of orca communication. We showcross-correlation methods can be a powerful tool for sound type classificationin combination with conventional methods.

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