AI model improves how companies measure acoustic material performance
Researchers have developed a neural network that significantly enhances the accuracy of sound absorption measurements for acoustic materials in real-world conditions. The breakthrough matters because manufacturers and building designers rely on these measurements to select materials for noise control—and better predictions mean more reliable soundproofing in offices, studios, and industrial facilities.
Originaltitel: A data-driven two-microphone method for measuring the sound absorption of finite absorbers
<p>A residual neural network is proposed to predict the sound absorption of an infinite rigidly-backed porous material from a classical two-microphone measurement above a finite porous sample. The network is trained using the microphones' transfer functions generated by a boundary element model (BEM), with a Delany-Bazley-Miki material model as a boundary condition. The network is validated numerically with BEM simulations and experimentally using two-microphone measurements of a baffled porous absorber of dimensions 60 cm×60 cm and 30 cm×60 cm, subject to various source locations. The results indicate that the network can significantly enhance the predictive capabilities of the classical two-microphone method. The suggested approach shows potential for accurately estimating the sound absorption coefficient of acoustic materials in realistic operational conditions.</p>