Investigating the structure of the greylag goose vocal repertoire: what can unsupervised methods tell us?
Lena Gies, Jonas Lesigang, Sonia Kleindorfer, Tecumseh Fitch · 2026
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Description
Lena Gies and colleagues mapped the greylag goose call repertoire in 2026 with unsupervised machine learning on a free-living population. Defining a species' call set by ear invites observer bias, yet manual scanning of large recordings is impractical. Four data representations were squeezed into 2, 20, and 100 dimensions and clustered by two standard methods plus graph-based Leiden detection.
The repertoire proved partly graded and broadly matched early human descriptions, with audio feature vectors beating spectrogram images for matching human labels. Leiden detection performed comparably and matched the human class count closest. Representation choice thus shapes repertoire analysis, and the study gives the first objective, quantitative portrait of greylag goose calls.
Catalog record
| CREATOR | Lena Gies, Jonas Lesigang, Sonia Kleindorfer, Tecumseh Fitch |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 57 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |