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[2511.08261] Uncertainty Calibration of Multi-Label Bird Sound Classifiers
Summary
This article evaluates the uncertainty calibration of multi-label bird sound classifiers, highlighting the challenges and improvements in bioacoustic classification accuracy.
Why It Matters
Accurate classification of bird sounds is crucial for biodiversity assessment. This study addresses the need for reliable uncertainty estimates in machine learning models, which can enhance decision-making in ecological research and conservation efforts.
Key Takeaways
- Calibration of bird sound classifiers varies significantly across datasets and classes.
- Models exhibit underconfidence or overconfidence, impacting classification reliability.
- Simple post hoc calibration methods can significantly improve model accuracy.
- A small labeled calibration set is effective for enhancing calibration.
- Evaluating uncertainty calibration is essential for improving bioacoustic classifiers.
Computer Science > Sound arXiv:2511.08261 (cs) [Submitted on 11 Nov 2025 (v1), last revised 24 Feb 2026 (this version, v2)] Title:Uncertainty Calibration of Multi-Label Bird Sound Classifiers Authors:Raphael Schwinger, Ben McEwen, Vincent S. Kather, René Heinrich, Lukas Rauch, Sven Tomforde View a PDF of the paper titled Uncertainty Calibration of Multi-Label Bird Sound Classifiers, by Raphael Schwinger and 4 other authors View PDF HTML (experimental) Abstract:Passive acoustic monitoring enables large-scale biodiversity assessment, but reliable classification of bioacoustic sounds requires not only high accuracy but also well-calibrated uncertainty estimates to ground decision-making. In bioacoustics, calibration is challenged by overlapping vocalisations, long-tailed species distributions, and distribution shifts between training and deployment data. The calibration of multi-label deep learning classifiers within the domain of bioacoustics has not yet been assessed. We systematically benchmark the calibration of four state-of-the-art multi-label bird sound classifiers on the BirdSet benchmark, evaluating both global, per-dataset and per-class calibration using threshold-free calibration metrics (ECE, MCS) alongside discrimination metrics (cmAP). Model calibration varies significantly across datasets and classes. While Perch v2 and ConvNeXt$_{BS}$ show better global calibration, results vary between datasets. Both models indicate consistent underconfidence, while AudioProt...
