A data-driven two-microphone method for in-situ sound absorption measurements

TL;DR

This paper introduces a neural network-based method using two-microphone measurements to accurately estimate the in-situ sound absorption coefficient of porous materials, enabling practical assessments in real-world conditions.

Contribution

It presents a novel data-driven approach that combines neural networks with traditional two-microphone measurements for in-situ sound absorption estimation.

Findings

Neural network accurately predicts sound absorption from transfer functions.

Method matches theoretical and impedance tube measurements.

Validated with experimental porous samples.

Abstract

This work presents a data-driven approach to estimating the sound absorption coefficient of an infinite porous slab using a neural network and a two-microphone measurement on a finite porous sample. A 1D-convolutional network predicts the sound absorption coefficient from the complex-valued transfer function between the sound pressure measured at the two microphone positions. The network is trained and validated with numerical data generated by a boundary element model using the Delany-Bazley-Miki model, demonstrating accurate predictions for various numerical samples. The method is experimentally validated with baffled rectangular samples of a fibrous material, where sample size and source height are varied. The results show that the neural network offers the possibility to reliably predict the in-situ sound absorption of a porous material using the traditional two-microphone method as if the sample were infinite. The normal-incidence sound absorption coefficient obtained by the network compares well with that obtained theoretically and in an impedance tube. The proposed method has promising perspectives for estimating the sound absorption coefficient of acoustic materials after installation and in realistic operational conditions.