用多源可靠性框架提升稀疏标签下的鸟类声音识别效果
Transfer Learning for Avian Bioacoustics under Sparse Positive Labels
- 构建多源可靠性模型，区分不同数据集的标注可信度
- 在BirdCLEF+2026上达到0.584宏平均精确率和0.860宏AUC
- 被动监测数据与生物知识选源带来显著性能提升
被动声学监测是生物多样性评估与野生动物保护的重要工具，支持大时空尺度的持续、非侵入式物种监测。然而，许多数据集存在稀疏正样本标签，即仅能确认某些物种存在，无法假设未标注物种不存在。本文在稀疏正标签下研究迁移学习，以BirdCLEF+2026为目标基准，使用BirdCLEF 2021、iNatSounds、WABAD和BirdSet作为外部生物声学来源。提出多源可靠性框架，将异质生物声学数据集建模为具有不同可靠性的独立监督源。该方法在公开BirdCLEF+2026验证集上取得0.584宏平均精确率和0.860宏AUC，优于简单拼接策略。最强增益来自被动声学监测数据集及基于生物学知识的源选择。结果表明，生物声学中的迁移学习本质上是弱监督与负迁移问题。
原文摘要 · Abstract (English)
Passive acoustic monitoring is an important tool for biodiversity assessment and wildlife conservation because it supports continuous and non-invasive monitoring of species across large spatial and temporal scales. Robust monitoring remains challenging because many datasets contain sparse positive labels, where species presences may be confirmed while unannotated species cannot be assumed absent. In this work, we study transfer learning under sparse positive labels using BirdCLEF+ 2026 as a target benchmark and BirdCLEF 2021, iNatSounds, WABAD, and BirdSet as external bioacoustic sources. We introduce a multi-source reliability framework that models heterogeneous bioacoustic datasets as distinct supervision sources with differing reliability. Our approach achieves 0.584 macro average precision and 0.860 macro AUC on public BirdCLEF+ 2026 validation labels while outperforming naive source pooling strategies. The strongest gains arise from passive acoustic monitoring datasets and biologically informed source selection. Our findings suggest that transfer learning in bioacoustics is fundamentally a weak supervision and negative transfer problem.
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