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基于深度残差学习的有限多孔样本吸声估计a).

Sound absorption estimation of finite porous samples with deep residual learninga).

作者信息

Zea Elias, Brandão Eric, Nolan Mélanie, Cuenca Jacques, Andén Joakim, Svensson U Peter

机构信息

The Marcus Wallenberg Laboratory for Sound and Vibration Research, Department of Engineering Mechanics, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden.

Acoustical Engineering & Civil Engineering Graduate Program, Federal University of Santa Maria, Santa Maria, Rio Grande do Sul, 97050-140, Brazil.

出版信息

J Acoust Soc Am. 2023 Oct 1;154(4):2321-2332. doi: 10.1121/10.0021333.

DOI:10.1121/10.0021333
PMID:37843379
Abstract

This work proposes a method to predict the sound absorption coefficient of finite porous absorbers using a residual neural network and a single-layer microphone array. The goal is to mitigate the discrepancies between predicted and measured data due to the finite-size effect for a wide range of rectangular absorbers with varying dimensions and flow resistivity and for various source-receiver locations. Data for training, validation, and testing are generated with a boundary element model consisting of a baffled porous layer on a rigid backing using the Delany-Bazley-Miki model. In effect, the network learns relevant features from the array pressure amplitude to predict the sound absorption as if the porous material were infinite. The method's performance is quantified with the error between the predicted and theoretical sound absorption coefficients and compared with the two-microphone method. For array distances close to the porous sample, the proposed method performs at least as well as the two-microphone method and significantly better than it for frequencies below 400 Hz and small absorber sizes (e.g., 20 × 20 cm2). The significance of the study lies in the possibility of measuring sound absorption on-site in the presence of strong edge diffraction.

摘要

这项工作提出了一种使用残差神经网络和单层麦克风阵列来预测有限多孔吸声器吸声系数的方法。目标是减轻由于有限尺寸效应导致的预测数据与测量数据之间的差异,适用于各种尺寸和流阻不同的矩形吸声器以及各种声源-接收器位置。训练、验证和测试数据是使用由刚性背衬上的带障板多孔层组成的边界元模型生成的,该模型采用德莱尼-巴兹利-米基模型。实际上,该网络从阵列压力幅值中学习相关特征,以预测吸声情况,就好像多孔材料是无限大的一样。该方法的性能通过预测的和理论的吸声系数之间的误差来量化,并与双传声器法进行比较。对于靠近多孔样品的阵列距离,所提出的方法至少与双传声器法表现相当,并且在低于400Hz的频率和小尺寸吸声器(例如20×20cm²)的情况下明显优于双传声器法。该研究的意义在于,在存在强边缘衍射的情况下现场测量吸声的可能性。

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