10:30
Human Response to UAS and UAM Noise
10:30
20 mins
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Comparison of aircraft sounds in a simulated Living Room laboratory study
Naomi Sieben, Aleyandra Smitt, Roalt Aalmoes
Abstract: Annoyance of aircraft sounds is strongly related to loudness of the event, but perception also varies with sound type. To examine the annoyance of different aircraft sounds, a participants study was conducted to measure perceived annoyance while conducting a math task and a colouring task during playback of these sounds in a living room setting using a multi-speaker-based playback system. Outdoor sounds were translated towards indoor sound levels considering an open-window setting. Five different aircraft sounds were evaluated, two helicopter sounds, two jet aircraft sounds, and one drone sound, that also varied in between 64-80 dB(A) peak sound level, based on their recorded sound level and the reduction of sound due to the propagation to the indoor environment. The drone sound and one of the jet aircraft were similar in peak sound level and therefore directly comparable, the other sounds were louder. Participants reported perceived annoyance and perceived disturbance after each event. Results show no significant differences in annoyance during the colouring or math task for the different sounds. Reported disturbance for the math task was higher than for the colouring task. Both helicopters, that had a higher peak sound level, were considered more annoying than the drone. But the (higher peak sound level) Airbus A330 was not significantly more annoying than the drone, nor was the Boeing 737, which was equal in peak sound level as the drone. Both helicopters and the Airbus A330 were reported significantly more disturbing than the drone sound. This study shows that drone sounds are not more annoying than other aircraft sounds if the sounds are played according to the representative sound levels within the tested indoor setting. The calculated propagation of the sounds towards the indoor sound, where higher (drone) frequencies are more absorbed than lower frequencies, may contribute to these findings. Future studies examining this effect, as well as a study including a larger set of tested drone sounds, will help to clarify these assumptions.
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10:50
20 mins
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Listening to UAS swarms: exploring the impact of swarm configuration attributes on annoyance perception
Jithin Thilakan, Lukas Aspöck, Janina Fels
Abstract: The acoustic impact of Unmanned Aerial Systems (UAS), such as drones, has received increased attention in noise research in recent years due to their expanding uses in delivery, mapping, and surveillance applications. The emergence of ‘UAS swarms’, involving multiple UAS operated in coordinated formations, further intensifies the concerns regarding noise pollution and annoyance perception in urban environments. Systematically designed variations of swarm configurations, including spatial distribution, spectral characteristics, and temporal dynamics, can lead to different perceptual impressions and potentially lower annoyance levels. As a first step to explore these relationships, a pilot experiment was conducted in which individual swarm configuration attributes were systematically varied to examine their impact on annoyance perception. The examined attributes include the number of drones in a swarm, their spatial distribution, inter-drone spacing, swarm velocity, and pitch-modulations of sources. These were tested under two baseline conditions featuring swarms with dark and bright timbral characteristics, mimicking large and small drone types. A set of swarm flyover scenarios, featuring controlled variations of individual swarm attributes, was simulated in a free-field condition and auralized as binaural audio samples. Perceptual evaluation was carried out to analyse the differences in the perceived annoyance ratings across sets of audio samples created through variation of individual swarm attributes. Furthermore, the influence of swarm attribute variations on the conventional acoustic and perceptual sound quality metrics derived from the sound samples was explored, complementing the results of the listening experiment and contributing to the acoustic optimization of swarm configurations.
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11:10
20 mins
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Blade Number Effects in sUAS Rotor Noise Assessed Using Psychoacoustic Metrics and an Online Listening Test
Seokhyeon Shin, Yeongmin Jo
Abstract: Blade number is a key design variable in sUAS and Urban Air Mobility (UAM) rotors in both aerodynamics and acoustics, yet the blade-number-induced changes in the spectral and temporal acoustic characteristics may not be captured by overall sound pressure level (OASPL) alone. In this study, we compare psychoacoustic metrics with subjective annoyance obtained from an online listening test for sUAS rotor noise with varying blade number.
We obtained the noise data from the low Reynolds number rotor measurements conducted in the ISAE-SUPAERO anechoic chamber. Each rotor has a NACA 0012 airfoil, 0.25 m diameter, and a fixed pitch angle of 10° without any twist, while the number of blades varies from 2 to 4. The noise was recorded at 1.62 m using a 13-microphone semicircular array spanning −60° to 60°. The operating points were selected close to the theoretically required RPM to approximate an equal-thrust condition. The psychoacoustic metrics (loudness, sharpness, roughness, fluctuation strength, and tonality) and psychoacoustic annoyance (PA) based on More (2010) were computed using the MATLAB-based open-source code, Sound Quality Analysis Toolbox (SQAT).
At −60° direction, OASPL and N_5 changed little with blade number, whereas 〖PA〗_5 increased as blade number increased. This trend was consistent with the increased sharpness for higher blade numbers, partially offset by reduced tonality.
In an online listening test, 2-blade and 4-blade sounds at −60° direction were level-scaled to nominal levels of 60-80 dB in 5 dB steps (10 stimuli). Based on 41 valid responses, annoyance ratings did not differ significantly between blade numbers at 60-70 dB, while the 4-blade condition was rated significantly more annoying at 75-80 dB. In addition, the main annoyance factor selections changed from tonality/roughness for 2-blade to sharpness for 4-blade, which are qualitatively consistent with the objective metrics.
Based on these results, we conclude that psychoacoustic metrics can complement sound level metrics in trade studies of blade numbers for sUAS/UAM rotor noise.
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11:30
20 mins
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Effects of night-time drone noise on sleep and annoyance
Susanne Bartels, Julia V. Lippold, Marcelo Sanchez Hernandez, Benjamin Aretz, Daniel Aeschbach
Abstract: Compared to conventional traffic noise, noise from drone manoeuvres has a high potential for disturbance and annoyance as recently found in listening experiments. However, the physiological effects of drone noise, particularly in relation to sleep, have not yet been investigated. As there are hardly any restrictions on the use of drones at night, we investigated the effects of drone noise on sleep and annoyance in a laboratory study with 37 subjects aged 20 to 65 years (24 female, 13 male).
In a crossover design, we exposed the subjects in a counterbalanced order during five consecutive nights to a) conventional aircraft noise, b) drone noise from over/passing flights, c) drone noise from take-offs, landings and hovering, d) road traffic noise and e) a control condition (no noise). During each noise night, 84 noises with maximum sound pressure levels of 35 to 60 dB(A) were presented via loud speakers. Sleep was measured via polysomnography. In addition, participants were asked about their perceived sleep quality as well as annoyance and disturbance due to the past night’s noise exposure. The study was double-blind and was announced as an investigation of the effects of environmental noise on sleep and annoyance. Neither the nature nor the timing of the noise events played during the night were known to the participants and experimenters.
Univariate analyses showed higher rates of awakening during nights with drone scenarios compared to the quiet control condition (p < 0.001) and the other noise nights. These findings were confirmed in linear mixed-effects models with random intercepts to account for inter-individual differences in baseline awakening rates. Self-assessed sleep quality was lower in nights with drone noise, while reported nocturnal annoyance and disturbance from noise was increased compared to the other modes of transport and the control condition. The current results highlight the increased potential for psychological and physiological adverse effects from drone noise and the need for early regulation of drone night-time operations to protect the public.
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11:50
20 mins
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Comparing a general sound quality metric for aircraft noise using a pre-trained neural network model to traditional psychoacoustics
Thiago Lobato, Tim Kamper-Schley, Marc Green, Max Ellis, Antonio Torija Martinez
Abstract: When dealing with sound perception, psychoacoustic is often the go-to solution to produce accurate and interpretable results. However, its meaning is very context-dependent, so that a general psychoacoustics-based sound quality metric for different sound types is challenging. This paper investigates a data-driven alternative to obtain sound-quality ratings of various types of aircraft noise using a pre-trained neural network model. As baseline traditional psychoacoustic approaches are used. The idea of using a pre-trained acoustic model is that it should be able to identify the context a sound is usually present and thus provide better predictions. All approaches are trained on diverse listening-test data from various datasets including diverse drones, airplanes and helicopters. Our psychoacoustic baselines use mainly psychoacoustic descriptors from the ECMA 418-2 standard as features to a regression models with different degrees of non-linearity, namely: linear models, KANNs, and Tree-based models (XGBoost). Those represent also different levels of interpretability from which an accuracy-interpretability trade-off is identified. Our results indicate that pre-trained acoustic representations can provide a more general solution for aircraft-noise sound-quality prediction, while non-linear regression on psychoacoustic features improves performance relative to linear baselines but may remain constrained by the lack of flexibility/contextual cues. These findings support the use of efficient pre-trained models as a practical route to general sound-quality metrics for aircraft-noise assessment. Additionally, if interpretability is desired, a hybrid approach of identifying annoying sounds with a neural network and then performing a fine-grained analysis with psychoacoustic parameters is also a promising approach
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12:10
20 mins
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Urban Air Mobility Noise Remote Test Response by Geographic Area and Comparison to In-Person Test
Siddhartha Krishnamurthy
Abstract: In September 2025, the National Aeronautics and Space Administration (NASA) completed the Varied Advanced Air Mobility (AAM) Noise and Geographic Area Response Difference (VANGARD) psychoacoustic test, which obtained responses from 359 geographically distributed participants to noise from a variety of passenger or equivalent cargo carrying Urban Air Mobility (UAM) vehicles. This test was designed to address insufficient data on human noise response from geographically distinct communities to varied UAM vehicles.
One of VANGARD’s test objectives, discussed in this paper, was designed to investigate UAM noise response differences between participants residing in areas of low and high noise soundscapes. Using participant postal codes, test administrators determined if each participant resided in a low or high noise soundscape. VANGARD test administrators did not mix any ambient noise with UAM vehicle noise. They investigated how much residing or working in a certain soundscape affected one’s annoyance response to UAM noise.
Analysis of the VANGARD test data revealed a response difference to UAM noise between low and high noise area participants. If substantiated with additional evidence, this result implies that while high noise areas may mask UAM noise, residents in these areas may be disposed to higher UAM noise annoyance when masking is not present (again, the VANGARD test did not mask UAM noise). Potential reasons for this VANGARD test result are proposed in the paper.
The paper will also compare VANGARD test responses with those from the NASA Helicopter and UAM Laboratory Comparison (HULC) psychoacoustic test, completed in July 2025, which compared conventional lightweight helicopter noise responses to noise responses from a variety of UAM aircraft. The HULC and VANGARD tests both used the same annoyance rating scale. A portion of the VANGARD test participant responses produced a UAM noise and annoyance trend line that was different from that produced by HULC test participant responses to UAM noise. If additional testing supports these results, they indicate that UAM noise annoyance models may require two or more response possibilities that relate to a given UAM noise metric.
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