In this paper, the measurement of the absorption coefficient of non-locally reactive sample layers of thickness d1 backed by a rigid wall is investigated. The investigation is carried out with the aid of real and theoretical experiments, which assume a monopole sound source radiating sound above an infinite non-locally reactive layer. A literature search revealed that the number of papers devoted to this matter is rather limited in comparison to those which address the measurement of locally reactive samples. Furthermore, the majority of papers published describe the use of two or more microphones whereas this paper focuses on the measurement with the pressure-particle velocity sensor (PU technique). For these reasons, the assumption that the sample is locally reactive is initially explored, so that the associated measurement errors can be quantified. Measurements in the impedance tube and in a semi-anechoic room are presented to validate the theoretical experiment. For samples with a high non-local reaction behavior, for which the measurement errors tend to be high, two different algorithms are proposed in order to minimize the associated errors.

This work proposes a method to predict the sound absorption coefficient of finite porous absorbers using a residual neural network and a single-layer microphone array. The goal is to mitigate the discrepancies between predicted and measured data due to the finite-size effect for a wide range of rectangular absorbers with varying dimensions and flow resistivity and for various source-receiver locations. Data for training, validation, and testing are generated with a boundary element model consisting of a baffled porous layer on a rigid backing using the Delany–Bazley–Miki model. In effect, the network learns relevant features from the array pressure amplitude to predict the sound absorption as if the porous material were infinite. The method's performance is quantified with the error between the predicted and theoretical sound absorption coefficients and compared with the two-microphone method. For array distances close to the porous sample, the proposed method performs at least as well as the two-microphone method and significantly better than it for frequencies below 400 Hz and small absorber sizes (e.g., 20 × 20 cm2). The significance of the study lies in the possibility of measuring sound absorption on-site in the presence of strong edge diffraction.

This study examines the edge diffraction effect when a sound wave impinges and reflects off finite porous absorbers, flush-mounted in an infinite hard baffle. A theoretical analysis of the diffraction is given by taking a two-dimensional spatial Fourier transform of a plane wave impinging on a finite absorber. Numerical experiments are also presented to simulate the sound field above infinite and finite locally reactive absorbers and the measurement with an array of pressure sensors. In such cases, a regularized solution is used to separate the incident and reflected plane wave components, in the wave-number domain, including both propagating and evanescent waves. The properties of the wave-number spectrum are associated either with the specular reflection or with the diffracted components, caused by the interaction of the sound wave with the finite absorber. From the regularized solution, it is possible to reconstruct the surface impedance and the absorption coefficient of the sample. The influence of Gaussian noise on such measurements is also investigated. The use of propagating and evanescent waves on the sound field model led to an estimation of the absorption coefficient that depends just slightly on the size of the sample, which is a desired feature for in situ measurement methods.

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 one-dimensional–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 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.