Year: 2026
Type: article
Abstract: Analyzing large datasets of animal vocalizations requires efficient bioacoustic methods for categorizing the vocalization types. This study evaluates the effectiveness of different vocalization clustering methods, comparing fully automated and semi-automated methods against the gold standard of manual expert annotations. Effective methods achieve good clustering performance whilst minimizing human effort. We release a new dataset of 1454 zebra finch vocalizations manually clustered by experts, on which we evaluate (i) fully automated clustering using off-the-shelf methods based on sound embeddings and (ii) a semi-automated workflow relying on refining the embedding-derived clusters. Clustering performance is assessed using the Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI). Results indicate that while fully automated methods provide a useful baseline, they generally fall short of human-level consistency. In contrast, the semi-automated workflow achieved agreement scores comparable to inter-expert reliability, approaching the levels of expert manual clustering. This demonstrates that refining embedding-derived clusters reduces annotation time while maintaining gold standard accuracy. We conclude that semi-automated workflows offer an optimal strategy for bioacoustics, enabling the scalable analysis of large datasets without compromising the precision required for robust behavioral insights.
Source: Applied Sciences
DOI: 10.3390/app16020810
Institutions: University of Zurich, ETH Zurich, Institute of Neuroinformatics
Language: English
Topic: Music and Audio Processing
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7123633179