资源环境科技发展态势分析平台

Natural habitats are being impacted by human pressures at an alarming rate. Monitoring these ecosystem-level changes often re-quires labor-intensive surveys that are unable to detect rapid or unanticipated environmental changes. Here we have developed a generalizable, data-driven solution to this challenge using eco-acoustic data. We exploited a convolutional neural network to em-bed soundscapes from a variety of ecosystems into a common acoustic space. In both supervised and unsupervised modes, this allowed us to accurately quantify variation in habitat quality across space and in biodiversity through time. On the scale of seconds, we learned a typical soundscape model that allowed automatic identi-fication of anomalous sounds in playback experiments, providing a potential route for real-time automated detection of irregular envi-ronmental behavior including illegal logging and hunting. Our highly generalizable approach, and the common set of features, will enable scientists to unlock previously hidden insights from acoustic data and offers promise as a backbone technology for global col-laborative autonomous ecosystem monitoring efforts.