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

2026-09-08

AI Summary

研究通过模型生成伪标签解决大量蝙蝠录音难以标注的问题，并提出属感知平滑法提高识别准确性。

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/

Problem

• Passive Acoustic Monitoring
• Bat Call Recognition
• Pseudo-Labels
• Semi-Supervised Learning
• Unlabeled Data

Innovation

• pseudo-labeling
• genus-aware smoothing
• semi-supervised learning