Prediction of sound absorption coefficient for metaporous materials with convolutional neural networks

Obtaining the airborne sound absorption coefficient is essential for studying the sound absorption performance and sound absorption mechanism of acoustic metamaterials. The most commonly used method for numerical calculation of airborne sound absorption coefficient is Finite Element Method (FEM). However, when the number of samples is relatively large, especially when the internal geometric structure of the samples is complicated, the calculation cost of FEM becomes exponentially high. Compared with FEM, machine learning algorithms show great potential in efficiently and intelligently predicting material properties. Taking images representing the topological structure of acoustic metamaterials (along with their airborne sound absorption performance simulated by FEM) as input, we propose a deep convolutional neural network to predict the broadband airborne sound absorption curve of the metaporous materials from 300 Hz to 3000 Hz with the interval of 50 Hz. To avoid overfitting, the network hyperparameter with favorable generalization capability is determined via constantly monitoring the overfitting level of the network. In addition, cross-validation is exploited to train the network to the best performance. Designed in such a compact manner where only one network is sufficient to predict for a whole absorption curve with a large range, the network is marvelously computationally economic and efficient and shows excellent prediction accuracy.