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Sound absorption estimation of finite porous samples with deep residual learning
KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Fluid Mechanics and Engineering Acoustics, Marcus Wallenberg Laboratory MWL.ORCID iD: 0000-0001-5723-9571
Acoustical Engineering & Civil Engineering Graduate Program, Federal University of Santa Maria, Santa Maria, Rio Grande do Sul, 97050-140, Brazil.ORCID iD: 0000-0002-7674-4407
School of Architecture, Rensselaer Polytechnic Institute, Troy, New York 12180, USA.
Siemens Industry Software, Interleuvenlaan 68, BE-3001 Leuven, Belgium.
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2023 (English)In: Journal of the Acoustical Society of America, ISSN 0001-4966, E-ISSN 1520-8524, Vol. 154, no 4, p. 2321-2332Article in journal (Refereed) Published
Abstract [en]

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 x 20 cm2). The significance of the study lies in the possibility of measuring sound absorption on-site in the presence of strong edge diffraction.

Place, publisher, year, edition, pages
Acoustical Society of America (ASA) , 2023. Vol. 154, no 4, p. 2321-2332
National Category
Fluid Mechanics and Acoustics Probability Theory and Statistics
Research subject
Vehicle and Maritime Engineering; Applied and Computational Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-338244DOI: 10.1121/10.0021333ISI: 001085116800003PubMedID: 37843379Scopus ID: 2-s2.0-85174925611OAI: oai:DiVA.org:kth-338244DiVA, id: diva2:1805397
Funder
Swedish Research Council, 2020-04668
Note

QC 20231017

Available from: 2023-10-17 Created: 2023-10-17 Last updated: 2023-11-30Bibliographically approved

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Zea, EliasAndén, Joakim

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Zea, EliasBrandão, EricAndén, Joakim
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