Drone Acoustic Analysis for Predicting Psychoacoustic Annoyance via Artificial Neural Networks

TL;DR

This paper explores the use of deep learning models to predict human-perceived annoyance caused by drone noise, aiming to improve noise management and public acceptance of UAVs.

Contribution

It introduces a novel application of deep learning for psychoacoustic annoyance prediction based on comprehensive drone acoustic data and characteristics.

Findings

Deep learning models effectively predict psychoacoustic annoyance.

Drone physical and operational features influence perceived noise levels.

Enhanced understanding aids in noise reduction and public acceptance.

Abstract

Unmanned Aerial Vehicles (UAVs) have become widely used in various fields and industrial applications thanks to their low operational cost, compact size and wide accessibility. However, the noise generated by drone propellers has emerged as a significant concern. This may affect the public willingness to implement these vehicles in services that require operation in proximity to residential areas. The standard approaches to address this challenge include sound pressure measurements and noise characteristic analyses. The integration of Artificial Intelligence models in recent years has further streamlined the process by enhancing complex feature detection in drone acoustics data. This study builds upon prior research by examining the efficacy of various Deep Learning models in predicting Psychoacoustic Annoyance, an effective index for measuring perceived annoyance by human ears, based on multiple drone characteristics as input. This is accomplished by constructing a training dataset using precise measurements of various drone models with multiple microphones and analyzing flight data, maneuvers, drone physical characteristics, and perceived annoyance under realistic conditions. The aim of this research is to improve our understanding of drone noise, aid in the development of noise reduction techniques, and encourage the acceptance of drone usage on public spaces.