Boundary conditions are a critical part of room acoustic simulations. In the case of ray tracing, absorption coefficients of nearly all materials are measured; provided. However, wave-based simulations face several issues. The first one is the variety of boundary conditions used. Depending on the method, surface impedance or admittance might be needed, either in the frequency or in the time domain, as an angle-dependent or averaged variable. This limitation hinders the development of a standard measured quantity for boundary conditions in wave-based simulations. In turn, this leads to the second issue encountered, which is the lack of widely available data to describe the characteristics of the different materials commonly found in rooms. In this study, a deep neural network has been trained to estimate the material properties of porous absorbers from their absorption coefficient in octave bands. These estimated material properties can then be used to calculate any boundary condition needed. This method thus allows to characterize the boundary conditions for any type of room acoustic simulation from the most commonly available data. Moreover, it provides a new tool to identify the sound absorber corresponding to a desired absorption profile during the design phase of a project. The training dataset in this study was generated from finite element method simulations. The poroelastic properties of the material, the sample thickness, as well as the depth of the air cavity backing the material were varied to create the training dataset.
