在三维城市环境中基于机器学习的无人机噪音传播预测
Jiahua He1, Qichen Tan1, Hongsen Bao1
1Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR 999077, People's Republic of China.
The Journal of the Acoustical Society of America
|August 5, 2025
概括
在城市中预测无人机噪音是很困难的. 一个新的AI模型准确地预测声音传播,将计算时间从小时缩短到几分钟.
科学领域:
- 声学和环境工程.
- 城市规划中的人工智能
背景情况:
- 预测无人机和城市空中交通 (UAM) 的噪音是复杂的,因为不同的车辆类型,声音源和城市环境.
- 声学评估的传统方法是计算密集型和耗时的.
研究的目的:
- 开发一种高效准确的方法,用于预测3D城市环境中无人机的声音传播.
- 利用深度学习来改善城市研究中的声学评估.
主要方法:
- 一个监督的U形神经网络,一种卷积神经网络 (CNN),被设计用于声音传播预测.
- 该网络包含五个不同的层,代表不同的城市高度,以监督学习过程.
主要成果:
- 训练好的神经网络准确地预测了各种城市环境中的声音传播.
- 在声压方面,平均误差为5%以下.
- 与Gaussian光束追踪等传统方法相比,计算时间从小时减少到几分钟.
结论:
- 开发的神经网络为预测城市地区无人机噪音提供了高效和准确的解决方案.
- 这种方法显示了加强城市声学研究和规划的巨大潜力.
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