A ready-to use workflow for automated, sound-based bird identification and localization

Field studies of bird communities typically require the fine-scale mapping of individuals. Passive acoustic monitoring combined with localization of individuals is a promising approach to gather such data. While various approaches for identification of species and localization of individuals have been proposed, no fully automated ready-to-use workflow is available so far. We here present a novel approach based on sound recordings with multiple cost-efficient automated recording units (Audiomoths). The workflow uses a well-established AI model (BirdNET; other models possible) for species identification and localizes the sources of all identified bird sounds with high accuracy. Tests with replayed sounds of different bird species in an agricultural landscape show that - after filtering out identifications with low identification confidence - the algorithm localizes more than 90% of the sounds within 5 m of the true location (85 % < 2 m). Recording and localization of wild birds demonstrate the applicability of the approach for avian ecology. This workflow is completely automated and ready-to-use, also for non-experts and can also be used when strong winds affect the speed of sound or if 3D localizations are of interest. By making data on individual bird locations accessible the presented work will help to advance fundamental and applied ecology as well as conservation.