Prediction of perceived annoyance caused by an electric drone noise through its technical, operational, and psychoacoustic parameters.

Abstract

Electric drones serve diverse functions, including delivery and surveillance. Nonetheless, they encounter significant challenges due to their annoying noise emissions. To address this issue, a sound database was created from experiments conducted in a hover-test-bench and real flights operated indoors. These experiments involved a wide range of parameter variations and operational conditions. A global digital user study involving 578 participants was conducted to assess drone noise annoyance. Furthermore, correlations between annoyance levels, psychoacoustic metrics, sociocultural factors, and technical/operational parameters were analyzed. The effects of implementing acoustic optimization modifications on the drone's performance were quantified with a conceptual design tool. The findings indicate that reducing the levels of loudness, sharpness, tonality, and roughness or fluctuation strength led to an improvement in annoyance. Differences in variable importance of psychoacoustic metrics dependent on the specific model were found. Sociocultural factors did not affect annoyance. Technical and operational parameters impacted annoyance, especially when reducing blade tip speed. A 20% reduction in tip speed showed potential through tool application as it maintained acceptable drone performance while beneficially targeting annoyance. A multi-disciplinary optimization is recommended to maintain operational efficiency. Last, psychoacoustic metrics were validated as an effective measure to evaluate a design solution.