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