构建欧洲夜行性候鸟鸣声数据集，助力鸟类保护研究
NBM: an Open Dataset for the Acoustic Monitoring of Nocturnal Migratory Birds in Europe
- 收集117种鸟类13,359段带时间频率标注的鸣声数据
- 自研两阶段深度模型在45个主要物种上达顶尖精度
- 开放数据与代码，推动生态音频研究协作
迁徙鸟类种群持续面临威胁，亟需有效的监测技术以支持保护工作。被动声学监测对难以追踪的夜行性迁徙物种尤为重要。本文发布诺特恩鸟迁徙（NBM）数据集，包含来自西帕莱尔蒂克地区117种鸟类的13,359段经标注的鸣声，每段均有精确的时间与频率信息，由数十位法国观鸟爱好者共同采集。该数据集支持新型下游声学分析。我们通过训练一种专为音频处理设计的两阶段深度目标检测模型，实现了对频谱图中鸟鸣的精准定位。该模型在数据集中45个主要物种上的表现与在更大规模音频集合上训练的先进系统相当。结果表明，推动此类开放科学倡议，获取昂贵但高价值的音频精细标注，具有重要意义。所有数据与代码均已公开。
原文摘要 · Abstract (English)
The persisting threats on migratory bird populations highlight the urgent need for effective monitoring techniques that could assist in their conservation. Among these, passive acoustic monitoring is an essential tool, particularly for nocturnal migratory species that are difficult to track otherwise. This work presents the Nocturnal Bird Migration (NBM) dataset, a collection of 13,359 annotated vocalizations from 117 species of the Western Palearctic. The dataset includes precise time and frequency annotations, gathered by dozens of bird enthusiasts across France, enabling novel downstream acoustic analysis. In particular, we prove the utility of this database by training an original two-stage deep object detection model tailored for the processing of audio data. While allowing the precise localization of bird calls in spectrograms, this model shows competitive accuracy on the 45 main species of the dataset with state-of-the-art systems trained on much larger audio collections. These results highlight the interest of fostering similar open-science initiatives to acquire costly but valuable fine-grained annotations of audio files. All data and code are made openly available.
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