MetaPerch通过引入元数据作为辅助信号，让生物声学模型学会了更多环境关联信息，在多个数据集上比现有模型更强。
MetaPerch是一种新的生物声学基础模型，通过利用Xeno-Canto等公民科学平台上的元数据（如地点和时间）作为辅助监督信号。该模型在17个生物声学数据集上评估了9种不同元数据源的效果，显著提升了物种识别性能。MetaPerch在多个挑战性领域达到了最先进的物种检测结果，尤其解决了实际被动声学监测中的物种分布和声学域偏移问题。
MetaPerch: Learning from metadata for bioacoustics foundation models
Bioacoustic foundation models rely on large-scale citizen science platforms like Xeno-Canto for geographically and ecologically diverse data. Recent work has shown that supervision alone can produce SotA species detection models when trained on this large-scale data -- however, there remains unutilized potential in the form of recording metadata readily available within these community-driven data hubs. In this work, we explore the use of metadata -- such as location and time -- as auxiliary supervision signals, allowing the model to leverage species-metadata correlations in its learned representation. Auxiliary metadata losses provide additional information beyond vocalizations alone that can encourage a richer, more robust representation that generalizes better to species distribution and acoustic domain shifts -- important challenges for deployment in real-world passive acoustic monitoring (PAM) settings. We introduce MetaPerch, a new foundation model that achieves strong species identification performance across multiple challenging domains and present an extensive empirical study of the effects of 9 diverse metadata sources on 17 bioacoustic datasets.