In situ aerial bioacoustic monitoring from extremely noisy drone recordings

Abstract Aerial bioacoustic monitoring has the potential to transform biodiversity surveys by enabling rapid acoustic sampling across large and inaccessible habitats. However, its application from unmanned aerial vehicles (UAVs) has remained limited because strong rotor self‐noise overwhelms target sounds such as bird vocalisations, compromising the reliability of ecological information derived from onboard recordings. We present a practical drone‐based acoustic sensing system that combines targeted hardware noise mitigation with a deep‐learning noise reduction workflow to recover bird vocal activity from highly noisy UAV recordings. The system incorporates a locally adaptive enhancement model trained on in situ noise characteristics, enabling robust deployment under real field conditions. Using in situ UAV recordings, bird vocalisations were recovered at flight altitudes of up to 30 m, despite input signal‐to‐noise ratios typically ranging from −20 to −30 dB. Noise suppression substantially improved automated bird detection rates using off‐the‐shelf species recognition software, and community similarity analyses showed that denoised aerial recordings closely matched ground‐based and ambient reference soundscapes. By restoring the ecological interpretability of drone‐based acoustic data, this work demonstrates that aerial bioacoustic monitoring can function as a practical and scalable tool for biodiversity assessment in challenging environments, enabling surveys in habitats that are otherwise difficult or impossible to access.