Issue |
Acta Acust.
Volume 8, 2024
|
|
---|---|---|
Article Number | 35 | |
Number of page(s) | 13 | |
Section | Virtual Acoustics | |
DOI | https://doi.org/10.1051/aacus/2024026 | |
Published online | 13 September 2024 |
Technical & Applied Article
Drone auralization model with statistical synthesis of amplitude and frequency modulations
Institute for Hearing Technology and Acoustics, RWTH Aachen University, Kopernikusstraße 5, 52074 Aachen, Germany
* Corresponding author: christian.dreier@akustik.rwth-aachen.de
Received:
7
May
2024
Accepted:
13
June
2024
This paper presents a drone auralization model that reproduces the spectro-temporal and spatial characteristics of a drone during flight. Focusing on perceptual plausibility, the time-variant processes are modeled by taking into account the statistical amplitude and frequency modulation distributions of a reference drone sound. For completeness, the far-field directivity is extracted based on time-variant wave backpropagation from microphone array signals. Both components consider a combined level calibration with regard to the reconstructed sound pressure on a spherical surface around the source. With regard to reproducibility, this paper is accompanied by supplemental data to present a synthesis model including the oscillator and digital filter coefficients for procedural audio synthesis. From evaluation, the model shows good agreement by comparison of psychoacoustic measures of the synthesized drone to a recorded reference. The drone auralization model can be applied in future research on urban soundscapes where Unmanned Aerial Vehicles (UAV) may appear in a great variety of use cases. Furthermore, it can deliver input data for simulation tools where the spatial radiation characteristics of a drone should be included, such as the development of array-based drone detection.
Key words: Auralization / Drone sound synthesis / Drone directivity / Modulation / Time-variant wave backpropagation
© The Author(s), Published by EDP Sciences, 2024
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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