Unlabeled Echoes: Pseudo-Labels and Genus-Aware Smoothing for Bat Call Recognition
Passive acoustic monitoring produces far more bat recordings than experts can label. We show that simple model-generated pseudo-labels turn this surplus into effective supervision. We compare pseudo-labeling with other semi-supervised learning methods on an 18-species European corpus using only 10% of its training labels, then transfer the strongest approaches to South African field audio containing nine bat taxa and a nuisance class. Pseudo-labeling outperforms the other semi-supervised learning methods on every European measure, recovering up to 61.5% of the gap to full supervision. It transfers to field audio with gains of 10.69 points in species accuracy and 4.96 points in species mac...
| Authors | Frank Fundel, Alexandra Howard |
|---|---|
| Primary category | cs.SD |
| Published | 8 September 2026 |
| Last updated at source | 8 September 2026 |
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