Issue |
Acta Acust.
Volume 4, Number 6, 2020
|
|
---|---|---|
Article Number | 24 | |
Number of page(s) | 10 | |
Section | Environmental Noise | |
DOI | https://doi.org/10.1051/aacus/2020023 | |
Published online | 11 November 2020 |
Scientific Article
Synthesis of real world drone signals based on lab recordings
1
Empa, Swiss Federal Laboratories for Materials Science and Technology, 8600 Dübendorf, Switzerland
2
armasuisse, Science and Technology, 3602 Thun, Switzerland
3
RUAG AG, 3770 Zweisimmen, Switzerland
* Corresponding author: kurt.heutschi@empa.ch
Received:
4
August
2020
Accepted:
10
October
2020
There is a great interest in the generation of plausible drone signals in various applications, e.g. for auralization purposes or the compilation of training data for detection algorithms. Here, a methodology is presented which synthesises realistic immission signals based on laboratory recordings and subsequent signal processing. The transformation of a lab drone signal into a virtual field microphone signal has to consider a constant pitch shift to adjust for the manoeuvre specific rotational speed and the corresponding frequency dependent emission strength correction, a random pitch shift variation to account for turbulence induced rotational speed variations in the field, Doppler frequency shift and time and frequency dependent amplitude adjustments according to the different propagation effects. By evaluation of lab and field measurements, the relevant synthesizer parameters were determined. It was found that for the investigated set of drone types, the vertical radiation characteristics can be successfully described by a generic frequency dependent directivity pattern. The proposed method is applied to different drone models with a total weight between 800 g and 3.4 kg and is discussed with respect to its abilities and limitations comparing both, recordings taken in the lab and the field.
© K. Heutschi et al., Published by EDP Sciences, 2020
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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