Passive acoustic monitoring in the Age of Artificial Intelligence and Big Data: Methods, Applications, and Biodiversity Conservation

Passive acoustic sensors now generate more biodiversity data than any team can process by hand. Machine learning and acoustic indices have rapidly become the standard tools for turning that raw sound into ecological knowledge. This Research Topic brings together methodological advances and field applications involving the automated analysis of sound recordings, from species detection and call recognition to acoustic indices, community-level inference, and long-term monitoring programmes. We particularly welcome contributions from Latin America and Iberia, regions of exceptional acoustic biodiversity where monitoring capacity is expanding quickly.We welcome Original Research, Reviews, Methods, and Perspective articles addressing, but not limited to:· Deep learning and machine learning approaches for automated species detection and call classification· Acoustic indices, community-level inference, and ecosystem monitoring· Passive acoustic monitoring programmes (design, deployment, and long-term datasets)· Benchmarking, validation, and reproducibility of automated acoustic methods· Integration of acoustic data with other sensor streams (camera traps, remote sensing) for biodiversity indicators· Applications to conservation, management, and the monitoring of threatened or data-deficient taxa Topic editors: Felipe N. Moreno-Gómez, Andrés Canavero Source: https://www.frontiersin.org/research-topics/85644/passive-acoustic-monitoring-in-the-age-of-artificial-intelligence-and-big-data-methods-applications-and-biodiversity-conservation