具有深度残留学习的有限多孔样本的声音吸收估计)
Elias Zea1, Eric Brandão2, Mélanie Nolan3
1The Marcus Wallenberg Laboratory for Sound and Vibration Research, Department of Engineering Mechanics, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden.
The Journal of the Acoustical Society of America
|October 16, 2023
概括
本研究介绍了一种神经网络方法,使用麦克风阵列来准确预测有限多孔材料的声音吸收系数. 这种方法克服了传统方法的局限性,特别是对于较小的样本和较低的频率.
科学领域:
- 声学 声学 在声学方面
- 材料科学 材料科学 材料科学
- 机器学习 机器学习
背景情况:
- 预测多孔材料的声音吸收对于控制噪音至关重要.
- 有限尺寸效应和边缘衍射引入测量的差异.
- 现有的方法难以准确地预测有限的吸收器.
研究的目的:
- 开发一种方法来预测有限多孔吸收器的声音吸收系数.
- 为了减轻由有限尺寸效应引起的差异.
- 为了能够准确地在现场进行声音吸收测量.
主要方法:
- 使用一个残余神经网络 (ResNet),通过边界元素模型 (BEM) 生成的数据进行训练.
- 采用单层麦克风阵列来捕获压力振幅数据.
- 使用德拉尼-贝兹利-米基模型进行多孔层模拟.
主要成果:
- 神经网络准确地预测声音吸收系数,从阵列压力中学习特征.
- 拟议的方法的性能与双麦克风方法相比或更好,特别是在400Hz以下和小型吸收器尺寸 (20x20cm2) 下.
- 在预测和理论声音吸收系数之间实现了减少误差.
结论:
- 拟议的方法有效地预测了有限的多孔吸收器的声音吸收.
- 它在特定条件下比双麦克风方法提供了显著的优势,包括低频率和小样本大小.
- 能够在现场进行可靠的声音吸收测量,即使有边缘衍射效应.
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