数据驱动的阻抗管方法用于预测正常的声音吸收系数)
Zu-Jie Yang1, Yong-Bin Zhang1, Liang Xu1
1Institute of Sound and Vibration Research, Hefei University of Technology, 193 Tunxi Road, Hefei 230009, People's Republic of China.
The Journal of the Acoustical Society of America
|April 2, 2025
概括
这项研究引入了一种新的数据驱动阻抗管方法,使用神经网络准确预测声音吸收系数. 这种方法克服了传统的局限性,使多模态声学场的有效测量成为可能.
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:
- 双麦克风阻抗管方法是声学参数测量的标准,但受到平面波假设的限制,限制了其有效的频率范围.
- 现有的方法与多模态声场作斗争,需要先进的技术来进行准确的声学分析.
研究的目的:
- 提出数据驱动的阻抗管方法,用于准确预测多模式场中的正常声音吸收系数.
- 为了克服传统的基于平面波的阻抗管测量所固有的频率范围限制.
主要方法:
- 将神经网络模型与阻抗管的传输关系集成.
- 通过限制物理模型边界条件来生成广泛的预训练数据集.
- 监督学习策略用于训练神经网络,将声压映射到振幅向量.
主要成果:
- 拟议的数据驱动方法准确预测正常的声音吸收系数.
- 模拟和实验验证证证实了该方法的预测能力.
- 在多模态声场内有效运行,超越传统的限制.
结论:
- 数据驱动的阻抗管方法为准确的声音吸收系数测量提供了强大的解决方案.
- 这种方法将阻抗管测试的适用性扩展到复杂的声环境中.
- 机器学习的整合为推进声学材料表征提供了一个强大的工具.
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