Efficient Diffraction Modeling Using Neural Networks and Infinite Impulse Response Filters
Loading...
Access rights
openAccess
acceptedVersion
URL
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
Date
2023-09
Department
Major/Subject
Mcode
Degree programme
Language
en
Pages
11
Series
AES: Journal of the Audio Engineering Society, Volume 71, issue 9, pp. 566-576
Abstract
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.Description
Funding Information: This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) under the “SCalable Room Acoustics Modelling” grant EP/V002554/1. Publisher Copyright: © 2023 Audio Engineering Society. All rights reserved.
Keywords
Other note
Citation
Mannall, J, Savioja, L, Calamia, P, Mason, R & Sena, E D 2023, ' Efficient Diffraction Modeling Using Neural Networks and Infinite Impulse Response Filters ', AES: Journal of the Audio Engineering Society, vol. 71, no. 9, pp. 566-576 . https://doi.org/10.17743/jaes.2022.0107