Song Meter Mini在基于未标记数据的南非鸟类自动生物声学监测中的应用
Abstract
Analyses for biodiversity monitoring based on passive acoustic monitoring (PAM) recordings is time-consuming and chal lenged 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.
摘要：
基于被动声学监测（PAM）记录的生物多样性监测分析是耗时的，并且受到记录中存在背景噪声的挑战。现有的声音事件检测（SED）模型仅适用于某些鸟类，进一步模型的开发需要标记数据。开发的框架自动从选定鸟类的可用平台中提取标记数据。标记的数据被嵌入到录音中，包括环境声音和噪声，并用于训练卷积递归神经网络（CRNN）模型。这些模型是在夸祖鲁-纳塔尔省城市栖息地记录的未经处理的现实世界数据上进行评估的。Adapted SED-CRNN模型达到了0.73的F1分数，证明了它在嘈杂的现实世界条件下的效率。所提出的自动提取选定鸟类物种标记数据的方法使PAM能够轻松适应其他物种和栖息地，以用于未来的保护项目。
关键词：Song Meter，鸟鸣记录，野生动物声学监测，鸟类声学记录，鸟类被动式声学监
