Chiara Semenzin

Semenzin, C., Mustun, F., Dessì, R., Emanuelli, A., Orhan, P., Lakretz, Y., de Polavieja, G., Sumbre, G. (2025). Dolph2Vec: Self-Supervised Representations of Dolphin Vocalizations. Preprint

Dolph2Vec, self-supervised, species-specific model significantly outperforms general-purpose baselines in detection and classification. The learned embeddings capture interpretable dolphin whistle categories, enabling fine-grained analysis of communication patterns.

Mustun, F.*, Semenzin, C.*, Rance, D., Marachlian, E., Guillerm, Z., Mancini, A., Bouaziz, I., Fleck, E., Shashar, N., de Polavieja, G., Sumbre, G. (2024). Whistle variability and social acoustic interactions in bottlenose dolphins. In Review

Signature whistles vary systematically, forming distinct sub-categories that mirror social structures and serve different communicative roles. Remarkably, dolphins were also observed producing the signature whistles of their deceased mothers—a behavior previously seen only in humans.

Semenzin, C., Hamrick, L., Seidl, A., Kelleher, B., Cristia, A. (2021). Towards large-scale data annotation of audio from wearables: validating zooniverse annotations of infant vocalization types. IEEE Spoken Language Technology Workshop (SLT), pp. 1079–1085.

Classification of individual vocalizations on Zooniverse was overall moderately accurate compared to the laboratory gold standard.

Semenzin, C., Hamrick, L., Seidl, A., Kelleher, B.L., Cristia, A. (2021). Describing vocalizations in young children: A big data approach through citizen science annotation. Journal of Speech, Language, and Hearing Research.

A large dataset of infant speech was uploaded on a citizen science platform. The same data were annotated in the laboratory by highly trained annotators. An analysis of descriptors defined at the level of individuals found strong correlations between descriptors derived from Zooniverse versus laboratory annotations.