Issue |
Acta Acust.
Volume 9, 2025
Topical Issue - Virtual acoustics
|
|
---|---|---|
Article Number | 1 | |
Number of page(s) | 15 | |
DOI | https://doi.org/10.1051/aacus/2024073 | |
Published online | 03 January 2025 |
Scientific Article
Source-time dominant modeling of the Doppler shift for the auralization of moving sources
1
Institute of Sound Recording, University of Surrey, Stag Hill, Guildford, GU2 7XH, UK
2
Applied Acoustics Branch, NASA Langley Research Center, Building 1208, Hampton, VA 23681, USA
* Corresponding author: r.ali@surrey.ac.uk
Received:
19
February
2024
Accepted:
16
October
2024
When developing an auralization for acoustic scenarios involving moving sources and receivers, one key feature is the ability to simulate the Doppler shift, i.e., the changing frequency content from the receiver’s perspective. As the time-varying delay between a source and receiver is what accounts for the Doppler shift, an approximation of this delay is required to successfully render the changes in frequency content at the receiver. Depending on the signal-processing strategy chosen to accomplish this task, there is, however, a potential to introduce audible artifacts due to frequency folding (aliasing), frequency replication (imaging), and broadband noise. In this paper we discuss the manifestation of such artifacts and propose a method to eliminate them, which can be integrated into the digital signal processing chain of larger auralization schemes. The method is built upon a source-time dominant approach and uses a combination of oversampling, interpolation, and time-varying filtering to predict and eliminate frequency regions at the receiver that are vulnerable to aliasing and imaging. We demonstrate the strengths and weaknesses of the method using a circularly moving source with a fixed receiver.
Key words: Auralization / Signal processing / Doppler shift / Sample rate conversion / Virtual acoustics
© The Author(s), published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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