Investigation of Metrics for Assessing Human Response to Drone Noise

Abstract

Novel electric air transportation is emerging as an industry that could help to improve the lives of people living in both metropolitan and rural areas through integration into infrastructure and services. However, as this new resource of accessibility increases in momentum, the need to investigate any potential adverse health impacts on the public becomes paramount. This paper details research investigating the effectiveness of available noise metrics and sound quality metrics (SQMs) for assessing perception of drone noise. A subjective experiment was undertaken to gather data on human response to a comprehensive set of drone sounds and to investigate the relationship between perceived annoyance, perceived loudness and perceived pitch and key psychoacoustic factors. Based on statistical analyses, subjective models were obtained for perceived annoyance, loudness and pitch of drone noise. These models provide understanding on key psychoacoustic features to consider in decision making in order to mitigate the impact of drone noise. For the drone sounds tested in this paper, the main contributors to perceived annoyance are perceived noise level (PNL) and sharpness; for perceived loudness are PNL and fluctuation strength; and for perceived pitch are sharpness, roughness and Aures tonality. Responses for the drone sounds tested were found to be highly sensitive to the distance between drone and receiver, measured in terms of height above ground level (HAGL). All these findings could inform the optimisation of drone operating conditions in order to mitigate community noise.

Requirements for Drone Operations to Minimise Community Noise Impact

“…However, until enough robust evidence on the human response to drone noise exposure is gathered, existing noise metrics and recommended target could be used to inform regulation of operational procedures. This assumption is also supported by some recent research suggesting loudness-related metrics as the main drivers of noise annoyance for drone operations [ 7 , 16 ].…”

“…However, this condition may not be present for other drone manoeuvres. The accelerating and deaccelerating drones’ mechanisms are mainly related to the rotational speed and tilting rotor position but produce changes in the pitch of the emitted sound; therefore, the drone operations can cause both spectral content and time variations of sound with consequences in the drone acoustics noise impact [ 30 ] and annoyance [ 16 ].…”

Urban Traffic Noise Analysis Using UAV-Based Array of Microphones

“…as low as possible [ 59 ]. In addition, a second direction of improvement that we are envisaging is to increase the directivity of the microphone array, based on recent research [ 60 , 61 , 62 , 63 , 64 , 65 , 66 ], and introduce the possibility to remotely configure it, according to local geometrical and environmental conditions.…”

sUAS noise characterisation during hovering operations

“…On the other hand, Sound Quality Metrics (SQMs) describe the psychoacoustic characteristics of signals that are useful from a noise perception perspective. This concept is important because sUAS noise has been credited as more annoying than conventional aircraft noise at the same loudness level, and sUAS noise annoyance is significantly related to loudness, sharpness, and fluctuation strength [5,6].…”