Search

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., (© 2023 Acoustical Society of America. All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).)

This paper proposes an experimental setup for measuring the sound radiation of a quadrotor drone using a hemispherical microphone array. The measured sound field is decomposed into spherical harmonics, which enables the evaluation of the radiation pattern to non-probed positions. Additionally, the measurement setup allows the assessment of noise emission and psychoacoustic metrics at a wide range of angles. The obtained directivity patterns using a third-order spherical harmonic decomposition (SHD) are shown to exhibit low distortion with respect to the original measurements, therefore, validating the SHD as an adequate representation strategy. Furthermore, the noise emissions are evaluated, and the highest noise emission is observed in the 90 ° azimuth direction. The exterior spherical acoustic holography description is employed to evaluate psychoacoustic metrics at arbitrary far-field positions and validated on a reference microphone. The estimated psychoacoustic metrics are closely related to the target metrics, which allows for sound quality analysis at any point external to the drone.

Recent studies have demonstrated that acoustic waves can be used to reconstruct the roughness profile of a rigid scattering surface. In particular, the use of multiple microphones placed above a rough surface as well as an analytical model based on the linearised Kirchhoff integral equations provides a sufficient base for the inversion algorithm to estimate surface geometrical properties. Prone to fail in the presence of high noise and measurement uncertainties, the analytical approach may not always be suitable in analysing measured scattered acoustic pressure. With the aim to improve the robustness of the surface reconstruction algorithms, here it is proposed to use a data-driven approach through the application of a random forest regression algorithm to reconstruct specific parameters of one-dimensional sinusoidal surfaces from airborne acoustic phase-removed pressure data. The data for the training set are synthetically generated through the application of the Kirchhoff integral in predicting scattered sound, and they are further verified with data produced from laboratory measurements. The surface parameters from the measurement sample were found to be recovered accurately for various receiver combinations and with a wide range of noise levels ranging from 0.1% to 30% of the average scattered acoustical pressure amplitude.

This paper proposes a method for estimating the angle-dependent sound absorption coefficient of a large material sample using a compact microphone array. The method relies on the description of the pressure field as a pair of in-going and out-going waves or using an image source model and stands as a generalization of the classical two-microphone method. The array includes an irregular spacing normal to the surface to avoid spatial aliasing. Furthermore, the benefit of additional microphones parallel to the sample is investigated, while keeping the array compact. The approach is validated against the transfer matrix method as well as against locally and non-locally reactive surface models and compared to the two-microphone method. The sensitivity of the estimation to uncertainties in the microphone positions is evaluated by means of a Monte Carlo approach. Measurements above melamine foam and gravel samples are presented and illustrate the reduced uncertainty in the sound absorption estimation. In particular, the proposed method exhibits improved robustness compared to the two-microphone method, especially at low frequencies.

A method to obtain the state matrix of an arbitrary linear homogeneous medium excited by a plane wave is proposed. The approach is based on projections on the eigenspace of the governing equations matrix. It is an alternative to manually obtaining a linearly independent set of equations by combining the governing equations. The resulting matrix has been validated against previously published derivations for an anisotropic poroelastic medium.

This paper presents a method for simultaneously identifying both the elastic and anelastic properties of the porous frame of anisotropic open-cell foams. The approach is based on an inverse estimation procedure of the complex stiffness matrix of the frame by performing a model fit of a set of transfer functions of a sample of material subjected to compression excitation in vacuo. The material elastic properties are assumed to have orthotropic symmetry and the anelastic properties are described using a fractional-derivative model within the framework of an augmented Hooke's law. The inverse estimation problem is formulated as a numerical optimization procedure and solved using the globally convergent method of moving asymptotes. To show the feasibility of the approach a numerically generated target material is used here as a benchmark. It is shown that the method provides the full frequency-dependent orthotropic complex stiffness matrix within a reasonable degree of accuracy.