用神经网络预测无人机噪声让人多烦，帮设计更安静的飞行器。
Drone Acoustic Analysis for Predicting Psychoacoustic Annoyance via Artificial Neural Networks
- 用深度学习模型分析无人机声学数据，输入包括飞行状态和物理参数。
- 基于真实环境测试数据，模型能准确预测人耳感知的烦躁程度。
- 适合关注无人机降噪、城市空中交通和人机交互的研究者。
无人飞行器（UAV）因成本低、体积小且易获取，在多个领域广泛应用。然而，其螺旋桨噪声已成为重要问题，可能影响公众接受度，尤其在居民区附近作业时。传统方法依赖声压测量和噪声特征分析，近年结合人工智能模型可更高效提取复杂声学特征。本研究通过多麦克风精确测量多种无人机型号的飞行数据、操作动作及物理特性，构建训练数据集，评估不同深度学习模型在预测心理声学烦躁度（Psychoacoustic Annoyance）方面的有效性。该指标用于衡量人类对噪声的实际感受。研究旨在深化对无人机噪声的理解，推动降噪技术发展，促进无人机在公共空间的广泛应用。
原文摘要 · Abstract (English)
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.
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