This study investigates the relationship between sound quality metrics (SQMs) and noise annoyance caused by airborne wind energy systems (AWESs). In a controlled listening experiment, 75 participants rated their annoyance on the International Commission on Biological Effects of Noise (ICBEN) scale in response to recordings from in-field measurements of two fixed-wing and one soft-wing ground-generation AWES. All recordings were normalized to an equivalent A-weighted sound pressure level of 45 dBA. The results revealed that sharpness was the only SQM predicting participants' annoyance. Fixed-wing kites, characterized by sharper and more tonal and narrowband sound profiles, were rated as more annoying than the soft-wing kite, characterized by higher loudness values. In addition, the effect of some SQMs on annoyance depended on participant characteristics, with loudness having a weaker impact on annoyance for participants familiar with AWESs and tonality having a weaker effect on annoyance for older participants. These findings emphasize the importance of considering psychoacoustic factors in the design and operation of AWESs to reduce noise annoyance.

The current study reports the results of a psychoacoustic listening experiment investigating the human response to the noise emissions from various types of drone flyovers, recorded during acoustic field experiments. The investigation covers six quadcopters with single propellers, a quadcopter with counterrotating propellers, and two types of hybrid electric vertical take-off and landing (eVTOL) drones. These recorded audio samples were employed in a dedicated listening experiment conducted at the Psychoacoustic Listening Laboratory (PALILA) in the faculty of Aerospace Engineering of Delft University of Technology, involving 57 participants. The two eVTOL drones were perceived as considerably less annoying than their quadcopter counterparts, whereas the coaxial-propeller quadcopter was found to be the most annoying drone. Strong correlations were found between the mass and volume of the quadcopter drones and the annoyance ratings from the listening experiments. Psychoacoustic annoyance metrics from different models proved to predict the perceived noise annoyance more accurately than conventional sound metrics typically employed in noise assessment.

Authorities are starting to pay attention not only to the noise levels of Unmanned Aerial Vehicles (UAVs) but also to their quality for acceptance. This manuscript presents a study of four types of propeller-driven UAVs (single-propeller quadcopter, coaxial-propeller quadcopter, quadplane eVTOL (electric vertical take off and landing) and tailsitter eVTOL) to assess their acoustic and psychoacoustic signatures. Experimental outdoor recordings are conducted under realistic flyover conditions. An acoustic analysis showed that quadcopters present higher noise levels compared to the eVTOLs, where the coaxial-propeller configuration revealed to be the noisiest and the quadplane the quietest. A psychoacoustic analysis demonstrated that the coaxial-propeller quadcopter was roughly three times more annoying than its single-propeller counterpart, whereas the quadplane and tailsitter eVTOLs showed similarly lower annoyance values. Additionally, the coaxial-propeller quadcopter exhibited the highest levels of loudness and impulsiveness, while the tailsitter had the lowest. Conversely, the tailsitter exhibited contrasting behavior in terms of sharpness. Regarding tonality, the quadplane was the most tonal, and the tailsitter eVTOL the least. In terms of modulation frequency characteristics, the single-propeller UAV emitted the harshest and most pulsating sound, while the tailsitter had lower values.

In recent years, the search for more sustainable propulsion systems for aviation has led to an increased interest in electric propulsion aircraft. These aircraft are often claimed to be more silent due to the lack of an internal combustion engine which eliminates the combustion and exhaust noise. However, the dominant noise source for single-propeller aircraft, independent of the propulsion method, is often the propeller itself. Additionally, lower noise levels simply assessed with conventional sound metrics like the sound pressure levels might not necessarily correspond to lower noise annoyance. Therefore, this research investigates the noise levels and expected psychoacoustic annoyance of the electric aircraft Pipistrel Velis in comparison to its fuel-burning counterpart, the Pipistrel Virus. The Velis can serve as a direct replacement for the Virus as they have the same number of seats and the same take-off weight, raising the question of what the effect of this replacement is on community noise annoyance. The research is based on experimental measurements during flyovers of both aircraft. Overall, it was found that the Velis is found to have lower noise emissions and lower psychoacoustic annoyance according to its sound quality metrics. This is reflected in the loudness, tonality, and impulsiveness, likely due to the presence of fewer tones below 500 Hz.

This manuscript summarizes the main recent research efforts at Delft University of Technology in the field of drone and urban air mobility (UAM) vehicle noise. Illustrative examples are showcased, specifically in terms of acoustic measurements (both in-field and in wind-tunnel facilities), noise modelling (both data-driven and physics-based), and human perception of these sounds. In particular, the measurements feature microphone arrays and acoustic imaging to detect, localize, and isolate drone noise emissions. Regarding drone noise modelling, the proposed approaches cover noise generation, propagation, and acoustic footprint calculation. The evaluation of the human perception of drone noise and the perceived annoyance is another crucial aspect. To this end, psychoacoustic listening experiments are conducted in laboratory conditions and the results are analyzed using perception-based sound metrics. Data from aeroacoustic measurements and synthetic sound auralizations are considered. Combining these three main approaches holistically, the perception-driven design and assessment can be performed by targeting the minimization of the perceived noise annoyance, rather than merely reducing sound pressure levels.

This study investigated the acoustic and psychoacoustic properties of five quadcopters drones during realistic flyover scenarios, utilizing a 64-microphone array for outdoor recordings. Acoustic analyses encompassed signal-to-noise ratio (SNR) values, time-frequency sound pressure levels, and noise spectra at overhead positions. An analysis based on A-weighted SNR revealed discernible drone noise despite background noise. Significant noise levels were observed up to 12 kHz. Harmonics of blade passage frequencies were evident, influencing noise spectra up to 1 kHz. Unlike traditional aircraft, drones' proximity to the ground limits the atmospheric absorption effects of high-frequency noise. A psychoacoustic analysis focused on sound quality metrics (SQMs) and annoyance assessment. SQMs exhibited consistent patterns across attributes, such as sharpness, tonality, roughness, and impulsiveness, with notable drone-specific perceptions. Different annoyance models indicated varying degrees of annoyance perception, with the Autel EVO II drone (lowest installation ratio, defined as the ratio between the drone diagonal size and the propeller diameter) perceived as the most annoying and the DJI Phantom 4 (heaviest) as the least one. Propeller positioning, represented by the parameter of installation ratio, correlated significantly with annoyance levels, suggesting an influence on both noise signature and psychoacoustic response. These findings highlight the importance of understanding the acoustic and psychoacoustic impact of drones, particularly in urban environments.

Correction Notice • Place s5 in front of the symbols ‘greater than or equal to (≥)’ and ‘less than (<)’ in Equation 2, as shown below. (Formula Presented). • Replace the correlation coefficient ‘0.886’ showed in Table 6 to ‘-0.886’. This correction also apply to the paragraph below Section 3 (Correlation between Annoyance and Drone Characteristics) and in the last paragraph of the Conclusions. This negative value indicates an inverse relationship between the installation ratio (d/D) and the annoyance computed using the More PA model. • Replace ‘0.337 (Zwicker PA model)’ by ‘0.362 (Willemsen PA model)’ in the paragraph below Section 3 (Correlation between Annoyance and Drone Characteristics). This value (0.362) is also reported in Table 6.