Drone Noise Assessment Methods and Noise containment approaches

Drones, also known as Unmanned Aerial Vehicles (UAVs), have rapidly integrated into various industries, from delivery services and agriculture to surveillance and disaster response. However, their increasing use has brought concerns about noise pollution, particularly in urban and residential areas. Unlike traditional aircraft, drones produce high-frequency, tonal noise, which can be more annoying to people. This study explores different methods for assessing drone noise, understanding its impact on communities, and identifying ways to mitigate it. This research also analyzed how drone noise propagates outdoors, how much enters buildings, and how it affects people indoors—especially during sleep. Factors like drone size, the number of rotors, flight maneuvers, and weather conditions significantly influence noise levels. A key part of this study involves comparing drone noise to other transportation sounds, such as road traffic, helicopters, and airplanes. Using listening experiments, participants are usually asked to rate how annoying and loud they found each type of noise. Interestingly, while drones are quieter than helicopters, their high-pitched, tonal noise makes them seem more irritating, especially in quieter environments. Many participants were uncomfortable with drones flying overhead, highlighting public skepticism and the need for better regulations. To address noise concerns, we investigated state-of-the-art noise reduction techniques such as optimizing propeller designs, adjusting flight patterns, and using active noise control technologies. Findings suggest that modifying drone operations and improving acoustic engineering can significantly reduce noise impact, making drones more socially acceptable. Overall, this thesis provides insights into drone noise assessment and its real-world effects. By developing clear guidelines and implementing noise reduction strategies, we can help integrate drones into everyday life while minimizing their disturbance to communities. Future work should focus on refining noise modeling techniques, improving public awareness, and working toward regulatory policies that balance technological progress with environmental and human well-being.