DeepForestSound: 一种用于非洲热带森林被动声学监测的多物种自动检测器，以基韦莱国家公园为例
DeepForestSound: a multi-species automatic detector for passive acoustic monitoring in African tropical forests, a case study in Kibale National Park
AI总结：
本文提出DeepForestSound模型，通过半监督方法结合聚类与人工验证，提升非洲热带森林中多物种声学监测的检测性能，尤其在非鸟类物种上表现优异。
摘要：
被动声学监测（PAM）广泛用于生物多样性评估，但在非洲热带森林中受限于标注数据稀缺，影响了通用生态声学模型对少数物种的性能。本文介绍DeepForestSound（DFS），一种专为非洲热带森林PAM设计的多物种自动检测模型。DFS采用半监督流程，结合未标注录音的聚类与人工验证，随后使用低秩适应对音频光谱转换器（AST）进行监督微调，与冻结骨干的线性基线（DFS-Linear）对比。该框架支持从长期声学记录中检测多个分类群，包括鸟类、灵长类和大象。DFS在乌干达基韦莱国家公园塞比托利区域训练，评估数据集在两年后于同一森林的不同地点记录。此评估评估了单一热带森林生态系统内时间与记录地点的泛化能力。在12个物种中，DFS在8个物种上优于现有自动检测工具，尤其在非鸟类物种上，平均AP值达到0.964（灵长类）和0.961（大象）。结果进一步显示，基于LoRA的微调在物种间显著优于线性探测。总体而言，这些结果证明了任务导向、区域特定的训练显著提高了声学复杂的热带环境中的检测性能，并突显了DFS作为非洲雨林生物多样性监测和保护实用工具的潜力。
英文摘要：
Passive Acoustic Monitoring (PAM) is widely used for biodiversity assessment. Its application in African tropical forests is limited by scarce annotated data, reducing the performance of general-purpose ecoacoustic models on underrepresented taxa. In this study, we introduce DeepForestSound (DFS), a multi-species automatic detection model designed for PAM in African tropical forests. DFS relies on a semi-supervised pipeline combining clustering of unannotated recordings with manual validation, followed by supervised fine-tuning of an Audio Spectrogram Transformer (AST) using low-rank adaptation, which is compared to a frozen-backbone linear baseline (DFS-Linear). The framework supports the detection of multiple taxonomic groups, including birds, primates, and elephants, from long-term acoustic recordings. DFS was trained on acoustic data collected in the Sebitoli area, in Kibale National Park, Uganda, and evaluated on an independent dataset recorded two years later at different locations within the same forest. This evaluation therefore assesses generalization across time and recording sites within a single tropical forest ecosystem. Across 8 out of 12 taxons, DFS outperforms existing automatic detection tools, particularly for non-avian taxa, achieving average AP values of 0.964 for primates and 0.961 for elephants. Results further show that LoRA-based fine-tuning substantially outperforms linear probing across taxa. Overall, these results demonstrate that task-oriented, region-specific training substantially improves detection performance in acoustically complex tropical environments, and highlight the potential of DFS as a practical tool for biodiversity monitoring and conservation in African rainforests.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
