Beyond Discrimination: Calibrated Geoprior Fusion for Bioacoustic Monitoring

Abstract:Modern bioacoustic foundation models like Perch and BirdNET can identify species with high discriminative accuracy, yet their confidence scores are often uncalibrated and difficult to interpret as probabilities of real-world occurrence. This limits their use for ecological inference beyond threshold-based detection. We leverage a global annotated acoustic dataset (WABAD) to produce calibration priors for an acoustic model, optionally incorporating species-level information. We introduce new methods of fusing the acoustic predictions with geopriors, which empirically improves calibration while preserving discrimination. Together, these results suggest a path to simpler and more reliable acoustic monitoring for broad biodiversity.

Audio and Speech Processing (eess.AS); Machine Learning (cs.LG); Sound (cs.SD)

arXiv:2609.35863 [eess.AS]

(or arXiv:2609.35863v1 [eess.AS] for this version)

https://doi.org/10.48550/arXiv.2609.35863 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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