Contribution
Inverse Acoustic Characterization of Porous Media using Artificial Neural Networks
* Presenting author
Abstract:
Porous sound absorbers can be described by the isotropic Biot model, and its fluid phase can be represented by the Champoux-Allard model. To apply these models to a given absorber material, the five acoustical and the four mechanical material parameters must be known. The direct measurement of these parameters is complex and requires cost-intensive measurement equipment. Current inverse methods to obtain these material parameters solve an optimization problem, trying to fit the absorption or impedance curve of the material model to the impedance tube measurement data. Solving this optimization problem, i.e. finding the global minimum, is not guaranteed in an acceptable amount of time, as the optimization problem possesses a multitude of local minima. This work proposes an alternative, data driven approach using artificial neural networks to obtain the material parameters necessary for the characterization of open porous materials. The approach only requires the results of standard impedance tube measurements. The characterization of rigid- and elastic frame materials has been investigated. The datasets were generated using the rigid and elastic frame models for porous absorbers. The approach shows good results for impedance curves generated by the analytical models, the validation with real-world impedance tube measurement data is currently under investigation.