Presentation at the Euronoise (e-Congress) 2021 of the paper "Learning the finite size effect for in-situ absorption measurement", authored by Elias Zea (KTH, Sweden), Eric Brandão (Federal University of Santa Maria, Brazil), Mélanie Nolan (DTU, Denmark), Joakim Andén (KTH, Sweden), Jacques Cuenca (Siemens, Belgium), and U. Peter Svensson (NTNU, Norway).
Abstract: In this paper we propose the use of neural networks to predict the sound absorption coefficient spectra of finite
porous samples with microphone arrays. The main goal is to train a model that can effectively mitigate the
errors caused by the finite size effect. A convolutional neural network architecture is used to map the array data
to the absorption coefficient at five frequencies. The training, validation and test data are numerically produced
with a boundary element method; modelling a baffled, locally reacting porous absorber on a rigid backing
with a Delany–Bazley–Miki model, for varying sample size, thickness, flow resistivity, incidence angle and
frequency. The strength of using machine learning in this context is that no hypotheses are made about the
sound field or the absorber, as the networks learn the necessary relationships from the data. We show that the
network approximates well the absorption coefficient, as if the sample was infinite, in a wide range of cases.
This work is financed by the Swedish Research Council (Vetenskapsrådet), grant agreement No. 2020-04668.