A Parameter-Free Few-Shot Evaluation for Elephant Vocalisation Classification
We present a parameter-free episodic evaluation of nearest-centroid classification of elephant vocalisations on fixed pretrained embeddings, for the Elephant Voices (EV) and Linguistic Data Consortium (LDC) datasets. We ask not which embedding yields the best classifier trained on all labelled data, but how the simplest classifier performs as the number of exemplars per class varies. There are no learnable parameters, because each class is modelled as the mean of its support embeddings and each query is assigned to the nearest centroid under squared Euclidean distance. Evaluation covers the fixed Perch (ver. 1), Perch (ver. 2) and HuBERT (base, layer 2) embeddings, alongside mel frequency...
| Authors | Christiaan M. Geldenhuys, Thomas R. Niesler |
|---|---|
| Primary category | eess.AS |
| Published | 14 August 2026 |
| Last updated at source | 23 September 2026 |
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