Machine Learning Based Auralization of Rigid Sphere Scattering

Wirler S, Schlecht SJ, Pulkki V (2021)

Publication Type: Conference contribution

Publication year: 2021

Publisher: Institute of Electrical and Electronics Engineers Inc.

Conference Proceedings Title: 2021 Immersive and 3D Audio: From Architecture to Automotive, I3DA 2021

Event location: Bologna, ITA

ISBN: 9781665409988

DOI: 10.1109/I3DA48870.2021.9610951

Abstract

In this paper, we present a method to auralize acoustic scattering and occlusion of a single rigid sphere with parametric filters and neural networks to provide fast processing and estimation of parameters. The filter parameters are estimated using neural networks based on the geometric parameters of the simulated scene, e.g., relative receiver position and size of the rigid spherical scatterer. The modeling differentiates an unoccluded and an occluded source-receiver path, for which different filter structures were used. In contrast to simulating occlusion and scattering numerically or analytically methods, the proposed approach provides rendering with low computational load making it suitable for real-time auralization in virtual reality. The presented method provides a good fit for modeling the acoustic effects of a rigid sphere. Further, a listening test was conducted, which resulted in plausible reproduction of the scattering and occlusion of a rigid sphere.

Authors with CRIS profile

Sebastian Schlecht

Involved external institutions

Aalto University Espoo FI Finland (FI)

How to cite

APA:

Wirler, S., Schlecht, S.J., & Pulkki, V. (2021). Machine Learning Based Auralization of Rigid Sphere Scattering. In 2021 Immersive and 3D Audio: From Architecture to Automotive, I3DA 2021. Bologna, ITA: Institute of Electrical and Electronics Engineers Inc..

MLA:

Wirler, Stefan, Sebastian J. Schlecht, and Ville Pulkki. "Machine Learning Based Auralization of Rigid Sphere Scattering." Proceedings of the 2021 Immersive and 3D Audio: From Architecture to Automotive, I3DA 2021, Bologna, ITA Institute of Electrical and Electronics Engineers Inc., 2021.

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