15:30
Session 6: Experimental Aeroacoustic Measurements – Field 3
Chair: Felix Hochbaum
15:30
20 mins
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Prediction of acoustic dynamics during horizontal flights of a contra-rotating propeller UAV
Camilo Ignacio Andino Cappagli, Alireza Amiri-Simkooei, Mirjam Snellen
Abstract: Noise emissions from unmanned aerial systems (UAS) in operational use exhibit
complex dynamics across multiple temporal scales, varying with maneuver state, UAS configuration,
payload, and atmospheric conditions. This variability limits the applicability of
noise models derived from quasi-stationary or laboratory-condition data, both for civil applications
such as noise certification and operational planning, and for downstream tasks
such as acoustic-based UAS detection and perception-driven design. While physics-based
aeroacoustic models provide valuable insight into noise-generation mechanisms, their applications
to real-world operations remain limited due to the complexity of the underlying interactions
and the uncertainty associated with operational environments. This study presents a
wavelet-based machine learning framework to predict the temporal dynamics of UAS acoustic
metrics from operational telemetry data. The framework is evaluated on data from forward
flight maneuvers collected during field campaigns under realistic operational conditions.
Telemetry variables and acoustic metrics representative of the first blade pass frequency
(BPF) are decomposed into wavelet coefficients, enabling the development of timescalespecific
predictive models. The proposed method accurately predict the signal trend and
the slowest wavelet components, achieving coefficients of determination of R² > 0.80. By
combining the trend and wavelet-level predictions, the complete model is then built from the
scale-specific models, achieving R² =0.77 and RMSE = 1.22 dB on the complete test acoustic
signal. These results highlight the potential of telemetry-driven methods to capture the
dominant acoustic dynamics of UAS operations and provide a practical tool for drone noise
prediction under realistic flight conditions.
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15:50
20 mins
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Measurements of outdoor drone noise
Christine Huth, Sonia Alves, Florin Herold, Julian Babl, Emilian Zehetbauer
Abstract: Currently available for the measurement of outdoor drone noise are
- the Guidelines on Noise Measurement of Unmanned Aircraft Systems by the European Union Aviation Safety Agency (EASA) released in June 2023 and
- the international Standard ISO 5350 Noise measurements of UAS (unmanned aircraft systems) – first edition 2024-01
To compare the procedures of both recommendations, measurements were carried out. The results will be discussed in particular with regard to their suitability in terms of human perception.
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16:10
20 mins
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On-board Acoustic Measurements on Drones: Self-Noise Characterization and Detection of External Sound Sources
Stefan Becker, Julian Benz
Abstract: On-board microphones on drones enable external sound acquisition without the need for ground-based arrays; they can be positioned flexibly and quickly to suit individual requirements and allow for wide-area sound measurements. However, measurement performance is significantly influenced by high self-noise, as well as by flow conditions, microphone arrangement and wind protection. This paper presents a measurement setup featuring a reference acoustic source and several reference microphones on the ground. Various reference signals are used at different measurement distances to validate the measurement capability. Furthermore, the measurement configuration and equipment on the quadcopter are explained, including the mounting of the on-board microphones and the measures taken to protect against flow-induced effects. Finally, the data processing is described, the aim of which is to reduce the influence of self-noise on the acoustic measurement. On this basis, the measurement capability is evaluated across a broad frequency spectrum, taking into account the influence of self-noise. From this analysis, the possibilities and limitations of the applied methodologies are ultimately identified.
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