Analyses for biodiversity monitoring based on passive acoustic monitoring (PAM) recordings is time-consuming and challenged by the presence of background noise in recordings. Existing models for sound event detection (SED) worked only on certain avian species and the development of further models required labeled data. The developed framework automatically extracted labeled data from available platforms for selected avian species. The labeled data were embedded into recordings, including environmental sounds and noise, and were used to train convolutional recurrent neural network (CRNN) models. The models were evaluated on unprocessed real world data recorded in urban KwaZulu-Natal habitats. The Adapted SED-CRNN model reached a F1 score of 0.73, demonstrating its efficiency under noisy, real-world conditions. The proposed approach to automatically extract labeled data for chosen avian species enables an easy adaption of PAM to other species and habitats for future conservation projects.

翻译：基于被动声学监测（PAM）记录的生物多样性监测分析耗时且受记录中背景噪声的挑战。现有的声音事件检测（SED）模型仅适用于特定鸟类物种，且进一步模型的开发需要标记数据。所开发的框架可从现有平台自动提取选定鸟类物种的标记数据。这些标记数据被嵌入到包含环境声音和噪声的录音中，并用于训练卷积循环神经网络（CRNN）模型。模型在夸祖鲁-纳塔尔城市栖息地记录的未经处理的真实世界数据上进行了评估。改进的SED-CRNN模型达到了0.73的F1分数，证明了其在嘈杂真实环境下的有效性。所提出的自动提取选定鸟类物种标记数据的方法，使得PAM能够轻松适应未来保护项目中的其他物种和栖息地。