Bioacoustic monitoring with machine learning (ML) models can provide valuable insights for informed decision-making in conservation efforts. In this study, the team built deep convolutional neural networks to analyze field recordings and classify calls of Yellow-vented warbler (Phylloscopus cantator) and Rufous-throated wren-babbler (Spelaeornis caudatus), both of which are regionally rare in Nepal. Data augmentation techniques for calls of the two bird species were utilized to effectively increase the size of the training set and thus boost model performance. Nepali ornithologists were engaged in iterative data labeling from field recordings, leveraging ML technology in conjunction with expert manual labeling and verification. The model output provides insights of species activity and abundance throughout 2018–2019 in multiple ecosystems along an elevational transect in the Barun River Valley, Nepal. The results of this study may help conservationists better understand species distribution, behavior, diversity, and habitat preference. Additionally, the results provide baseline data to quantify future changes due to habitat disruption or climate change. This modeling methodology and its framework can be easily adopted by other acoustic classification problems.

使用机器学习 (ML) 模型进行生物声学监测可以为保护工作中的明智决策提供有价值的见解。在这项研究中，该团队建立了深度卷积神经网络来分析野外录音并对黄腹莺（Phylloscopus cantator）和红喉鹪鹩（Spelaeornis caudatus）的叫声进行分类)，两者在尼泊尔地区均属罕见。两种鸟类叫声的数据增强技术被用来有效地增加训练集的大小，从而提高模型性能。尼泊尔鸟类学家通过实地记录进行迭代数据标记，利用 ML 技术与专家手动标记和验证相结合。模型输出提供了整个 2018-2019 年在尼泊尔巴伦河谷海拔横断面的多个生态系统中物种活动和丰度的见解。这项研究的结果可能有助于保护主义者更好地了解物种分布、行为、多样性和栖息地偏好。此外，结果提供了基线数据，以量化由于栖息地破坏或气候变化导致的未来变化。