发表机构
Google DeepMind; Harvard University（谷歌DeepMind; 哈佛大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对生物声学基础模型置信度未校准的问题，利用WABAD数据集生成校准先验，并融合地理先验，在保持判别能力的同时提升校准效果，为广域生物多样性监测提供更可靠的路径。
AI 中文摘要
现代生物声学基础模型如Perch和BirdNET能够以高判别精度识别物种，但其置信度分数往往未经校准，难以解释为现实世界出现的概率。这限制了它们在基于阈值的检测之外的生态推断中的应用。我们利用一个全球带注释的声学数据集（WABAD）为声学模型生成校准先验，并可选地整合物种级信息。我们引入了将声学预测与地理先验融合的新方法，实验证明该方法在保持判别能力的同时改善了校准效果。这些结果共同表明了一条通往更简单、更可靠的广域生物多样性声学监测的路径。
英文摘要
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.
Comments10 pages, 2 figures
