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

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.