The increase in flight volumes in the aviation industry has significant socioeconomic implications that affect different aspects of our communities and economies. Although it has great economic benefits, it also causes annoyance and disturbance to communities living near airports. The latter requires understanding and prediction of the varying noise levels generated by various aircraft types. Noise assessment on a fleet level is traditionally achieved by using prediction models such as the DOC29. Such models need to be validated using real measurements. For Amsterdam Schiphol Airport, the so-called NOMOS (Noise Monitoring System) with 39 measurement stations is used for this purpose. We analyze the time series of these stations, collecting annual data for the period from 2006 to 2023. The main objective is to determine how the aircraft-generated noise at these stations can be assigned to 13 different aircraft types, taking into account the different noise levels produced by each aircraft type. This is performed by time series analysis of individual stations and the averaged time series over all stations. The results from two least-squares methods, namely unconstrained least squares (LS) and a proposed bounded least squares subject to weighted constraints (BLS + WC), are compared. The constraints are based on certification data as prior information in the least squares method, which is expected to enhance the model's performance. Based on the above two least squares methods, predictions are performed for 2022 and 2023. The results clearly demonstrate the superiority of the BLS + WC over the LS method. We further extend our analysis to predict noise levels for a hypothetical future year with more newer aircraft models. The results indicate a substantial reduction in the noise level compared to 2023. These findings can thus underscore the effectiveness of the proposed method in outperforming the LS and highlight the model's capability to forecast the impact of fleet modernization on noise reduction.

Seabed backscatter data acquired by the multibeam echosounder (MBES) have been identified as a valuable indicator of sediment properties and benthic community characteristics. However, developing robust change detection models with MBES backscatter remains challenging due to the high costs and limited spatial coverage of seabed ground truth data. Lack of absolute backscatter calibration also hinders the comparison between repeated MBES measurements. To mitigate these issues, we propose an unsupervised method to detect seabed changes by fitting a Gaussian Mixture Model to the backscatter difference between two datasets. A relative calibration is conducted based on a stable reference area to eliminate the impact of possible drifts in echosounder characteristics on the backscatter difference. We then model the unchanged class as a zero-mean Gaussian distribution, with its variance constrained by the backscatter uncertainty estimated from the reference area. By processing each incident angle individually, the angular range with the greatest ability for seabed change detection can also be investigated. We demonstrate the effectiveness of the proposed method through two case studies in the Dutch North Sea. The detected changes reveal seasonal and temporal variations in benthic communities, such as sand mason worms, and are consistent with the sediment movement in one of the study areas. This research highlights the value of MBES backscatter data for seabed change detection and provides a cost-effective solution for seabed habitat monitoring with acoustic measurements.

The normalized fan rotational speed per aircraft engine (N1%) is an essential input parameter to noise prediction models, but is often confidential and not directly accessible to researchers. The aircraft acoustic signal characteristics, and specifically the tonal component, can be used to extract this parameter. However, existing methodologies estimate N1% parameters from whole-aircraft spectra, which can lead to inaccurate estimations. This research aims at investigating the various tonal contributions by isolating and reconstructing spectrograms of individual noise sources using acoustic arrays. Using such arrays, it is possible to discriminate between the various components that contribute to the noise emitted by the aircraft, especially between the engines, but also the nose landing gear. From the resulting engine-specific spectrograms the N1% of individual engines for 24 aircraft were obtained. For the A321neo and the B737NG, it is found that, for 80% of the analyzed aircraft, additional engine tones accompany the higher harmonics of the engine blade passage frequency, with these additional tones corresponding to twice the shaft frequency. In addition, it was found that N1% differences between the two engines are reflected in the spectrograms and that a tone stemming from the nose landing gear can be present, resulting in a complex pattern of tones in the whole-aircraft spectrogram. The insights on the various tonal contributions to the received signal are of importance regarding the further development of methods that aim to extract the engine setting from aircraft noise measurements and as such for enabling more accurate noise calculations.

This paper proposes a measurement-based methodology for investigating the ground effect observed in real-life aircraft flyover measurements in low-noise regions. An acoustic array is used for obtaining a spectrogram of the sound with ground reflections to a large extent eliminated. By applying a model to account for the ground effect, a semi-synthetic spectrogram is obtained. This spectrogram is then qualitatively compared with the spectrogram obtained from single microphone measurements (Munisense) at two heights to verify whether indeed the semi-synthetic spectrogram agrees with those measured by corresponding results. As a next step, the ground effect for the flyovers measured is quantified by using the array-recorded signals together with the model for the ground effect for all flyovers. By correcting Munisense signals for the estimated ground effect, improved agreement between noise levels at both receiver heights and the array measurements is obtained. Noise differences are evaluated using the proposed alternative metric that only accounts for levels within 4 s duration. For flyovers with higher elevation angles, a greater reduction in noise, up to 2.3 dBA, is observed, despite the larger slant distances. In contrast, flyovers at lower elevation angles show a smaller reduction of approximately 1.8 dBA. This contrasts with the common corrections applied for lateral attenuation in best-practice modeling where the attenuation (accounting for more than only the ground effect) diminishes at higher elevation angles.

This paper investigates the prediction accuracy and time efficiency of two distinct low-fidelity methods for predicting the tonal and broadband noise of a drone rotor in axial and non-axial inflow conditions. These are both derived from an aerodynamic rotor model based on the blade element momentum theory, respectively coupled with a time- and a frequency-domain solution of the Ffowcs Williams-Hawkings integral equation applied to a radial distribution of acoustically compact and non-compact sources. Experimental data and scale resolving
lattice-Boltzmann/very-large eddy simulation results for a two-bladed small unmanned aerial system in transitional boundary layer conditions are used to validate the low-fidelity approaches. Comparison between low-fidelity, high-fidelity and experimental results reveal that the underlying sound generation mechanisms are accurately modeled by the low fidelity methods, which therefore constitute a valid tool for the preliminary design of quiet drone rotors and for the estimation of the community noise impact of drone operations.

Satellite-derived bathymetry (SDB) provides a cost-effective solution for coastal mapping, but challenges remain in model interpretability and uncertainty quantification. This study investigates the applicability of the least-squares-based deep learning (LSBDL) framework for SDB, leveraging its hybrid structure that integrates neural networks with the available least-squares theory to enhance model transparency. ICESat-2 photon-counting LiDAR was used to train depth estimation from Sentinel-2 multispectral imagery over an approximately 30 km × 30 km region of near-coastal bathymetry at Anegada, British Virgin Islands. ICESat-2 provided high-precision depth information, of which 80% were used for training and the remainder for validation. LBSDL depth estimation achieved a root-mean-square error (RMSE) of 2.74 m, representing around 10% of the maximum observed depth, with the best performance in the 2–15 m depth range. These findings demonstrate the potential of LSBDL for interpretable and reliable bathymetric mapping, highlighting ICESat-2 as a globally accessible training and validation source and advancing SDB capabilities for data-sparse coastal regions.

The multibeam echosounder (MBES) has been widely used in seabed mapping, considering its ability to collect continuous and broad-scale seabed measurements efficiently. The presence of shellfish or dead shell material can alter the geophysical properties of the sediment and thus affect the MBES backscatter intensity, making acoustic surveys with the MBES a potential non-invasive solution for regularly monitoring the benthic habitats of shellfish aggregations. Although there exists an increasing interest in mapping marine benthos with MBES measurements recently, the use of multi-spectral backscatter data is still limited. Thus, this research aims to enhance the acoustic mapping of benthic habitats using multi-spectral MBES data, with a focus on a shell bed region in the Dutch North Sea. With backscatter measurements from three frequencies, 90, 300, and 450 kHz, we achieved seabed classification in two steps. First, a semi-supervised backscatter completion was conducted to generate full-coverage backscatter data for each incident angle, mitigating the limited overlap between adjacent survey lines. We then classified the multi-Angle backscatter data from each individual frequency using the Gaussian Mixture Model. Our results indicate an improved seabed classification performance compared to the classical Bayesian method. Comparisons of classification maps across frequencies also show their different abilities to distinguish the shell bed region from other coarse sediments, demonstrating the value of leveraging multi-spectral backscatter data in seabed habitat mapping.

Understanding acoustic characteristics of aircraft is critical for designing optimal fleet compositions in terms of noise and improved airport operations. This study investigates acoustic signatures across different aircraft types, engine designs, and operational conditions. A dataset consisting of 457 field acoustic measurements of commercial turbofan aircraft landing and taking-off from Amsterdam Airport Schiphol was used. To unveil meaningful patterns, we focused on dimensionality reduction techniques—Principal Component Analysis (PCA) and tdistributed Stochastic Neighbour Embedding (t-SNE)— to analyse this high-dimensional acoustic data. These methods are complemented by clustering algorithms and supervised machine learning models, such as K-Means, random forests for feature importance, and multilayer perceptrons (MLP) to classify aircraft types, engine configurations, and operations. Results reveal a strong loudness axis in the first principal component, overshadowing subtle spectral and timebased differences across aircraft families, especially for takeoffs. Nonetheless, focusing on higher-order components and alternative embeddings (t-SNE) highlights additional spectral and temporal markers. Operation classification (landing vs. takeoff) achieves 98% accuracy, but aircraft and engine family classification remain challenging, with accuracy capped below 50% using these feature sets. These findings suggest that advanced feature selection and dimensionality reduction while considering amplitude characteristics are essential for disentangling nuanced design-based acoustic traits.

Many highly interdependent disciplines are concerned with the system noise assessment of small propeller aircraft concepts, including aircraft, propeller, engine design and acoustic modelling, flight trajectories calculation, noise propagation and ground effect modelling. There are no state-of-the-art simulation processes in Europe which account for all the aforementioned disciplines, as efforts heretofore have been focused on large transport aircraft concepts. This paper presents the development of such a simulation process at the German Aerospace Center (DLR). The simulation process inputs top-level aircraft design requirements, producing a valid CS-23 conceptual propeller aircraft design. It then calculates realistic flight paths for departure and approach for the conceptual aircraft and simulates noise immissions at user-specified locations. The immissions assessment would then guide design modifications, low-noise flight trajectory generation and novel aircraft design. A Reims-Cessna F406 is considered as the reference aircraft and is used to obtain the first results from the simulation process. The results are compared with measurements from a flight test campaign using the same aircraft. Important effects observed in the results and in the immissions assessments are also presented in this paper. Furthermore, the capabilities of the simulation chain will be demonstrated with sensitivity studies that show the effects of modifying operational procedures on ground noise immissions.

To regulate aircraft noise impact on communities surrounding airports, best-practice models are used to predict aircraft noise levels. This research evaluates the noise–power–distance (NPD) tables employed in the European Doc 29 noise model using the noise measurements taken around Amsterdam Airport Schiphol. Thrust estimation is based on extracting the blade passing frequency from acoustic measurements and converting it to the engine rotational speed indicator N1%. The N1% estimates are validated with onboard flight data. Even with accurate input parameters (thrust and distance to the observer), discrepancies are observed between modelled and measured noise levels, which can be attributed to the inaccuracies in the NPD tables. To further investigate this, empirical thrust-noise relations are derived from the measurements. These derived relations are found to differ from those in the original NPD tables. When the empirical thrust-noise relations are used, the agreement between the modelled and measured mean noise levels improves. The standard deviation of the differences gets reduced by 25% for departure operations. This finding is subsequently confirmed using independent measurements around Oslo Airport Gardermoen. Beyond improving current best-practice noise modelling, the methodology presented in this research offers insight into the development and validation of NPD tables.

Sound propagation in closed test section wind tunnels suffers from reflections and diffraction, which compromise acoustic measurements. In this article, it is proved possible to improve the post-processing of phased-array microphone measurements by using an approach based on the combination of numerical acoustic simulations and beamforming. A Finite Element Method solver for the Helmholtz equation is used to model the acoustic response of the experimental facility. The simulations are compared with acoustic experiments performed at TU Delft's Low Turbulence Tunnel, using both fully reflective (baseline) and lined test sections. The solver accurately predicts the acoustic propagation from a monopole sound source at the centre of the test section to the microphones in the phased-array, for frequencies in the range 500Hz<f<2000Hz. It is shown that a (lower fidelity) geometric modelling method is unable to precisely predict the acoustic response of the Low Turbulence Tunnel at these frequencies, due to strong acoustic diffraction. The numerical results are used to implement corrections to the post-processing of experimental data. A corrected version of the Source Power Integration method is able to increase the accuracy of the source's noise levels calculation, based on a single numerical simulation with the source at the same location as in the experiment. A Green's function correction increases the beamforming resolution and the source's noise levels estimation accuracy from the beamforming maps, without a priori knowledge of the source's location. Both corrections perform well at processing flow-on acoustic measurements, and the Green's function correction shows an additional benefit. The improvement in beamforming spatial resolution leads to an increase of the signal to noise ratio.

For models that evaluate aircraft noise, thrust is an essential input. From aircraft flight recorder data or measured noise spectra, the engine's rotational speed can be estimated for which a conversion is then needed to obtain the engine's thrust. This research investigates three conversion methods. The first uses the expressions from the ANP database while the second method is based on the fuel flow. The third employs Gas Turbine Simulation Program (GSP) predictions. The thrust estimates are compared to airline performance calculations where significant variations up to 3 dBA in predicted noise were found. Methods one and three were found to be in good agreement with the performance data. An important finding of this paper is that combining methods one and three using least-squares is capable of providing the required conversion expressions, in line with those in the ANP database, but without being limited to a few aircraft types only.

Aircraft noise has a severe impact on communities around airports. For noise regulation, yearly average noise levels LDEN are modelled for large areas around the airport. To validate and improve the noise model, noise monitoring terminals (NMT) can be used. These NMTs, however, are often placed further away from the airport and in areas with higher background noise levels than ideal measurement conditions. Thresholds placed on the NMTs prevent them from capturing too much background noise but also prohibit them from measuring lower noise levels from aircraft. This research addresses the potential effects of undetected flights on the measured LNight and LDEN . For this, the Doc 29 modelling method is used. The case study is Schiphol Airport, where 41 NMTs are placed at different distances from the runways. Analysis of undetected flights showed that newer aircraft, such as the A320-NEO, were often not measured. Applying weighted least-squares to the available noise level measurements and supporting data gives insights into the possible aircraft-induced noise levels of undetected flights. These insights are used to improve the alignment between measured and modelled LNight and LDEN .

This study covers three aspects of acoustic localisation of drones using a microphone array. First, it assesses a grid-free approach, using differential evolution, to estimate the three-dimensional position of a drone. It is found that this is indeed possible for the drone in the near-field. For larger distances, it still provides the angular position of the drone. Second, the study emphasizes the essence of localisation over small frequency bands with the bands jointly spanning a large frequency range to reveal the presence of multiple sound sources and maximise the drone localisation range. Third, it addresses the localisation ranges for six different drones.

The growth shown by the aviation industry has given significant economic benefits, but also causes disturbance to communities living near airports, including annoyance and potential health problems due to the high aviation-induced noise levels. Therefore, regulations are implemented by imposing limits on the yearly cumulative noise levels at specific locations around airports. This requires understanding and prediction of the varying aircraft noise levels. This is traditionally achieved by using so-called best-practice or regulatory models, such as the Dutch aircraft noise model, which require low computational costs and limited model inputs. This way of noise monitoring comes with significant model approximations and hence potential deviations of the model predictions from the actual noise levels. The limitations of this current approach has given rise to distrust in communities near airports. Hence, the models need to be validated against real measurements, for which use is made of the stations from the Noise Monitoring System (NOMOS) around Schiphol Airport. In this contribution, we analyzed the time series of the yearly averaged L_den measured at 35 NOMOS stations for the period from 2006 to 2022. A distinction is made between noise from aircraft and that due to other noise sources. We observe a decreasing trend of 0.52 ± 0.04 dB(A)/year in L_den for the investigated time period. One of the objectives is to determine how the measured L_den can be assigned to the various aircraft types in the fleet at Schiphol. This is performed by an L_den time series analysis of the averaged time series over all stations, combined with changes in the fleet composition. The results from the unconstrained least squares (LS) and non-negative least squares (NNLS) methods are presented and compared. Based on the obtained model for 2006-2020, predictions are performed for 2021 and 2022.

Aircraft noise impacts a growing number of residents around airports. The impact is estimated using noise models such as the European standard Doc 29. These models make use of empirically derived Noise-Power- Distance tables to estimate noise in the areas around the airport. Correction factors are used to account for directionality effects such as engine installation effect and lateral attenuation. Research comparing measured and modelled directionality of the aircraft noise is limited. This research aims to investigate the potential contribution of directionality effects on differences between measured noise levels, obtained around Schiphol Airport using the NOise MOnitoring System (NOMOS), and noise levels predicted by Doc 29. This is done by considering NOMOS measurements at different locations around the airport and use these to retrieve noise levels at the source, i.e., the so-called standardized levels. These noise levels are then used to map the noise levels in lateral and longitudinal directions. By performing the same procedure for modelled sound levels, it is possible to observe differences in directionality patterns. The thus found directivity effects differ significantly from the currently modelled directivity effects. These insights can be used to increase the accuracy of the Doc 29 model without increasing its complexity.

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.

To regulate aircraft noise impact on communities surrounding airports, best-practice models are used to predict aircraft noise levels. In this research the Noise-Power-Distance (NPD) tables in the European Doc.29 noise model are evaluated with measurements around Amsterdam Airport Schiphol. Even with accurate input parameters (thrust and distance to the observer), differences in modelled and measured noise levels are found, which are assumed to be due to the errors in the NPD table. To further investigate this, thrust-noise relations are derived from measurements. These relations are found to differ from the original NPD tables. Using the measured thrust-noise relations, the modelled and measured mean noise levels are in agreement and the standard deviation of the differences is reduced by 25% for departure operations. This finding is consequently verified with independent measurements around Oslo Airport Gardermoen. Next to an improvement in best-practice noise modelling, the methods described in this research give insight into the creation and validation of NPD tables.

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.

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.