Creating plausible geometric acoustic simulations in complex scenes requires the inclusion of diffraction modeling. Current real-time diffraction implementations use the Uniform Theory of Diffraction, which assumes all edges are infinitely long. The authors utilize recent advances in machine learning to create an efficient infinite impulse response model trained on data generated using the physically accurate Biot-Tolstoy-Medwin model. The authors propose an approach to data generation that allows their model to be applied to higher-order diffraction. They show that their model is able to approximate the Biot-Tolstoy-Medwin model with a mean absolute level difference of 1.0 dB for first-order diffraction while maintaining a higher computational efficiency than the current state of the art using the Uniform Theory of Diffraction.

Authors: Mannall, Joshua; Savioja, Lauri; Calamia, Paul; Mason, Russell; De Sena, Enzo
Affiliations: Department of Music and Media, University of Surrey, Guildford, UK; Aalto University, Department of Computer Science, Espoo, Finland; Reality Labs Research at Meta, Redmond, WA, USA; Department of Music and Media, University of Surrey, Guildford, UK; Department of Music and Media, University of Surrey, Guildford, UK(See document for exact affiliation information.)
JAES Volume 71 Issue 9 pp. 566-576; September 2023
Publication Date: September 13, 2023 Import into BibTeX
Permalink: https://www.aes.org/e-lib/browse.cfm?elib=22232

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E-Library Location: (CD JAES71)   /jaes71/9/pg566.pdf

DOI: https://doi.org/10.17743/jaes.2022.0107

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