Silent Drones: A Deep Learning Approach to Suppress Drone Propeller Noise

Unmanned Aerial Vehicles (UAVs) provide many benefits and opportunities across a range of sectors, including surveillance, humanitarian work, disaster management, research, and transportation. Due to their accessibility and affordability, they are now used more than ever, which also poses some challenges. This is the noise pollution produced by the motors and propellers that has been highlighted as a significant issue to the people's health and the environment. To address this issue, this paper proposes to use Generative Adversarial Networks (GAN) to produce an inverse sound signal based on the drone's acoustic signals and use that to cancel the noise produced by the drone. We synthesize training data spanning the acoustic diversity of drone noise: steady-state propeller tones, rapid throttle transitions (simulating ascent/descent), and superimposed broadband turbulence. The GAN model is capable of adapting to dynamic settings, learning from data, and adjusting to testing conditions accordingly. We compared our proposed solution with other techniques that can also be used for drone signal interference in order to suppress the drone noise. This research idea paves the way for the need to address the issue created due to drone noise and a solution in managing this problem for modern drone applications.