Marc C. Green

Dr. Marc C. Green serves as a Postdoctoral Research Fellow within the School of Science, Engineering & Environment at the University of Salford, affiliated with the Acoustics Innovation Institute. His research bridges acoustic engineering, machine learning, and environmental psychology with focus on unmanned aircraft systems noise impact assessment.

• PhD in Music Technology, University of York (2020)

Dr. Green's research investigates how drone noise interacts with urban soundscapes through innovative methodologies including soundwalk assessments in controlled environments, psychoacoustic laboratory testing, and deep learning model development. His work specifically examines time-variant noise characteristics, flight operation impacts, and environmental interactions (such as pine tree barriers in urban parks) to understand human annoyance responses. The integration of spatial audio techniques with machine learning enables novel approaches to predicting UAV sound affect beyond traditional sound pressure level metrics.

Analysis of his 11 recent publications (2024-2025) reveals a strong focus on practical solutions for urban drone integration, with 55% dedicated to psychoacoustic modeling, 27% to field assessment methodologies, and 18% to technical noise characterization. His work consistently addresses the tension between societal benefits of drone technology and community noise impacts.

• EPSRC connected nation pioneers prize for 'Creative Computing for the Digital Economy' (for EigenScape database development)

Currently leading the RefMap project to reduce UAS environmental impact, Dr. Green's research receives direct institutional support through the Acoustics Innovation Institute. His EigenScape database continues to serve as a foundational resource for global soundscape researchers. The systematic review framework he developed for UAS noise impact testing provides regulators with evidence-based assessment protocols.

Dr. Green operates within the Acoustics Innovation Institute's collaborative ecosystem, leveraging specialized recording equipment and computational resources. His research group maintains comprehensive drone noise datasets across multiple platforms and develops open methodologies for community noise assessment in evolving urban air mobility scenarios.