Unlabeled Echoes: Pseudo-Labels and Genus-Aware Smoothing for Bat Call Recognition

• URL: http://arxiv.org/abs/2609.11986v1
• Date: Tue, 08 Sep 2026 09:40:12 GMT
• Title: Unlabeled Echoes: Pseudo-Labels and Genus-Aware Smoothing for Bat Call Recognition
• Abstract summary: We show that simple model-generated pseudo-labels turn this surplus into effective supervision.<n>We compare pseudo-labeling with other semi-supervised learning methods on an 18-species European corpus.<n>Pseudo-labeling outperforms the other semi-supervised learning methods on every European measure.
• Score: 47.7282395820107
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 macro-F1. We also introduce genus-aware smoothing, which directs uncertain target mass toward congeneric species. Combined with uniform smoothing, it reaches 79.16 species macro-F1, 4.73 points above hard targets. Simple pseudo-labels are therefore highly effective at this ecological data scale, while genus-aware targets inject useful biological structure at no annotation cost. https://code4conservation.github.io/UnlabeledEchoes/

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