Experimental validity of using a ResNet to predict sound absorption coefficients of finite samples

This is the authors' abstract. We don't add key points for this paper.

The validity of using a neural network to predict sound absorption coefficients of finite porous materials is tested with experimental data with a flush-mounted glass wool sample on a baffle. The network is pre-trained and validated with numerical simulations of flushed-mounted finite absorbers using a Delany-Bazley-Miki model. The experimental setup consists of a 12 x 12 microphone array placed above the absorber and a sound source placed at angles of 0, 40, and 75 degrees with respect to the normal of the sample. The sound absorption coefficients predicted at normal incidence by the network are compared with the impedance tube method as a reference result.