High diversity seabed habitats, such as shellfish aggregations, play a significant role in marine ecosystem sustainability but are susceptible to bottom disturbance induced by anthropogenic activities. Regular monitoring of these habitats with effective mapping methods is therefore essential. Multibeam echosounder (MBES) has been widely used in recent decades for seabed characterization due to its non-destructive manner and extensive spatial coverage compared to traditional methods like bottom sampling. Nevertheless, bottom sampling remains essential to link ground truth with acoustic seabed classification. Using seabed samples and MBES measurements, machine learning techniques are commonly employed to model their relationships and generate classification maps of an extended seabed. However, limited ground truth data, resulting from constraints in regulations, budget, or time, may impede the development of robust machine learning models. To address this challenge, we applied a semi-supervised machine learning method to classify seabed sediments of a blue mussel (Mytilus edulis) cultivation area in the Oosterschelde, the Netherlands. We utilized nine boxcore samples to generate pseudo-labels on MBES data. These pseudo-labels enlarged the training data size, facilitated the training of three comprehensive machine learning algorithms (Gradient Boosting, Random Forest, and Support Vector Machine), and helped to classify the study site into mussel and non-mussel areas. We found the geomorphological and backscatter-related features to be complementary for mussel culture detection. Our classification results were demonstrated effective through expert knowledge of this cultivation area and brought insights for future research on natural mussel habitats.

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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.

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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.@en

Aircraft noise is a significant problem for communities surrounding airports. Accurate prediction models are needed to estimate noise levels from aircraft operations. In this research, the accuracy of the sonAIR aircraft noise model in predicting noise levels from departures around Schiphol airport is evaluated by comparison to measurement data from NOMOS and the current best-practice modelling approach Doc29. Results show a significant but consistent underestimation of noise levels by sonAIR, mainly due to a generalisation of emission models. The standard deviation of differences between model results and measurements is lower for sonAIR than for Doc29 by up to 1 dB. Differences between measurement and model results were found in the relation between N1 and noise levels, and for maximum noise levels. The results demonstrate that sonAIR provides more reliable predictions of noise levels on the single flight event level than Doc29. Additionally, this study shows agreement with results from a previous validation study in Zürich, thereby demonstrating the applicability of sonAIR to another airport. This research contributes to better aircraft noise predictions, which will have implications that ultimately lead to a better quality of life for communities affected by aircraft noise.@en

To facilitate the conservation of seafloor habitats and planning of offshore activities, there is a growing need for mapping marine benthos in an effective and efficient way. Acoustic data acquired by multi-beam echosounders (MBES) have been extensively used for large-scale and high-resolution seafloor characterization. A deeper understanding of the relationship between backscatter data and sediment compositions can help to identify the benthos occurrence from the MBES data. With two multi-spectral MBES datasets collected near the western Wadden Sea islands in the North Sea, we investigated the potential of mapping marine benthos through backscatter classification. Two unsupervised classification methods, i.e., Bayesian classification, which mainly exploits the backscatter strength from incident angles larger than 20°, and hierarchical clustering of the backscatter strength at different angular ranges, were employed and the results were compared. The classification results from both methods showed a good correspondence with sediment properties such as the median grain size. Moreover, based on a principal component analysis of bottom sample properties, the hierarchical clustering results indicated a better distinction between contributions from the gravel content and benthos occurrence, e.g., sand mason worm density, than Bayesian classification, through involving the backscatter angular variations. Classification for multiple frequencies, on the other hand, showed little difference regarding the relationship with bottom sample properties. Although the backscatter difference between frequencies was also found to positively correlate with certain sample properties, using multi-spectral features for acoustic classification in this study did not reveal additional information compared to single-frequency classification results.@en

Accurate modelling of aircraft noise in different weather conditions is crucial for the reliability of noise predictions and their application worldwide. In best-practice aircraft noise models, such as Doc.29, the change in expected sound level on the ground due to changing atmosphere is modelled with a simplified propagation calculation. The Aircraft Noise and Performance (ANP) database contains several standardised source spectra, known as spectral classes, which are used for these calculations to account for the frequency dependence of the atmospheric effects. The spectral classes consist of Pressure Band Levels (PBL) of 24 1/3rd octave bands. This research focuses on the agreement of these spectral classes with measurements, taken around Amsterdam Schiphol Airport, and quantifies the effect of differences in these spectra for the Doc.29 weather correction. The measurements, taken by an acoustic array close to the runway and by continuous single microphone noise measurement stations (NOMOS) at long range, are propagated to the standard distance of 1000 ft (for which the ANP spectral classes are given) taking into account the geometrical spreading and the actual atmospheric absorption. For the B737-800 and A330, differences in shape are found between the two measured spectra and the spectral class. This is partly due to the low signal-to-noise ratio for the high frequencies in case of large distances between the aircraft and the measurement system. The effect of the application of the measured spectra on the Doc.29 weather correction is found to be smaller than 0.5 dBA for the NOMOS positions, indicating the suitability of the current ANP spectral classes for the weather correction.@en

The design, development, and acoustic characterization of the Psychoacoustic Listening Laboratory (PALILA) recently established at Delft University of Technology are presented in this manuscript. This laboratory comprises a soundproof room with a modular design and specialized audio equipment. Its primary objective is to conduct experimental investigations into the human perception of aeroacoustic noise sources, such as aircraft, drones, or wind turbines. Furthermore, PALILA is certainly suited for studying other sound sources (e.g. household appliances, ground vehicles, etc.). The manuscript outlines the fundamental characteristics of the facility (i.e. dimensions and materials). A thorough acoustic characterization is provided, including assessments of the background noise levels, reverberation time, free-field sound propagation, and transmission losses of the walls (with respect to the exterior). Overall, PALILA is deemed to be a suitable quiet environment to conduct high-quality psychoacoustic listening experiments.

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Full-scale propeller measurements are useful to study the total noise contribution of a propeller-driven aircraft, including installation effects. Full-scale measurements under operational conditions also provide an accurate validation opportunity for propeller noise prediction models. These studies are, therefore, necessary to quantify and reduce the noise annoyance of propeller-driven aircraft. For propeller aircraft, rotating sources need to be considered. In this research, propeller noise is studied for a full-scale propeller using an acoustic microphone array. The acoustic imaging techniques used are Conventional Frequency-Domain or Time-Domain Beamforming for the stationary noise sources and the ROtating Source Identifier for the rotating noise sources. By applying these two acoustic imaging methods simultaneously, in addition to filtering in the spatial and frequency domain, also filtering in the source velocity domain can be exploited. These methods were applied to an engine run-up of a Pipistrel Velis Electro, the first fully-electric certified aircraft. This electric aircraft, placed on the ground, allows for an initial study on the present noise sources and their relative contributions. Ultimately, this information can be used to separate the measured spectrum into spectra of different noise components which in turn can be used for full-scale validation and improvement of propeller noise prediction models.@en

Complex acoustic systems typically present three-dimensional distributions of noise sources. Conventional acoustic imaging methods with planar microphone arrays are unsuitable for three-dimensional acoustic imaging, given the computational demands and the incapability to explicitly account for the presence of multiple sources. This paper proposes the use of global optimization methods to solve these shortcomings. An experiment with three incoherent speakers proved that this method can accurately determine the three-dimensional location and the respective sound level of each individual source. In addition, super-resolution is achieved beyond half the Rayleigh resolution limit. VC 2022 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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Noise annoyance caused by wind turbines is a critical issue for the societal acceptance of wind energy. Wind turbine noise exposure is typically assessed using conventional time–averaged metrics, however, the literature suggests that these metrics do not fully capture the sound properties responsible for the perceived noise annoyance. Therefore, it is questionable to assess wind turbine noise and its abatement strategies using only such metrics. This paper presents a novel psychoacoustic model for predicting wind turbine noise annoyance that combines perception–based sound quality metrics. To establish the psychoacoustic model, the synthetic sound signals of two different wind turbines equipped with four state–of–the–art noise reduction add–ons (two types of trailing–edge serrations and two types of trailing–edge permeable materials inserts) were studied. Using a parametric wind turbine noise generator, the simulated sound signals were auralized at different observer positions and their noise annoyance was evaluated in two laboratory listening experiments with 16 and 10 subjects, respectively. The psychoacoustic annoyance model proposed here provides a very close agreement with the results of the listening experiment and improved accuracy compared to conventional sound metrics. @en

Realistic predictions of the contribution of the uncertainty sources affecting the quality of the bathymetric measurements prior to a survey is of importance. To this end, models predicting these contributions have been developed. The objective of the present paper is to assess the performance of the bathymetric uncertainty prediction model for Phase Difference Bathymetric Sonars (PDBS) which is an interferometric sonar. Two data sets were acquired with the Bathyswath-2 system with a frequency of 234 kHz at average water depths of around 26 m and 8 m with pulse lengths equal to 0.0555 ms and 0.1581 ms, respectively. The comparison between the bathymetric uncertainties derived from the measurements and those predicted using the current model indicates a relatively good agreement except for the across-track distances close to the nadir. The performance of the prediction model can be improved by modifying the term addressing the effect of footprint shift, i.e., spatial decorrelation, on the bottom due to fact that at a given time the footprints seen by different receiving arrays are slightly different@en

The contribution of the engine and the airframe to the total noise generated by an aircraft varies with the operating conditions. Semi-empirical models are able to account for such variations but require detailed engine and airframe data as input that is not readily available for most aircraft types and operations. This hinders the validation of these models through comparison between predictions and experimental data. This work investigates the sensitivity of semi-empirical models of engine and airframe noise to slight variations of the input data, representative of uncertainties in geometrical parameters and variability of the aircraft operating conditions during flyovers. In addition, the predictions are compared to measurements of A320, A330, and B777 landings and departures. This, together with the sensitivity analysis, indicates frequency regions where a mismatch between measurements and predictions exists. The deviation between predictions and measurements for landings can be partially explained by the underestimation of the sound pressure level of the higher harmonics of the fan. For takeoff, the models predict lower levels than measured. This is hypothesized to be associated with jet-installation noise, which is not accounted for in the semi-empirical models. The predicted spectra of the Airbus A320 and A330 were adjusted to account for jet installation noise, using levels available in the literature. This resulted in a better agreement between modeled and measured spectra at low frequencies.@en

The impressive growth of the aviation industry and the number of flights entail several environmental repercussions, such as increased aircraft noise emissions. With the worrying number of complaints from the communities around airports comes also the distrust in numerical models used for aircraft noise prediction. In this study, we compare the ‘Dutch aircraft noise model’ predictions to measured values from the NOise MOnitoring System (NOMOS) around Amsterdam Airport Schiphol between 2012 and 2018. While the model underestimates aircraft noise in 2012, the model prediction improved throughout the years. We observe a decreasing trend of measured aircraft-related L_den values of 0.6dB(A)/year (a total of 3.6dB(A) over the investigation period), although the total number of flight movements increased during the observation time. We propose that a change in fleet mix, as well as the implementation of Noise Abatement Procedures at Schiphol Airport, fuelled this trend.

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Sound absorbing porous materials are used to line a wind tunnel wall, in order to reduce reflections. However, the lining can have a detrimental effect on the acoustic measurements due to an increase in the noise radiated from the walls. In addition, the aerodynamic fidelity of the tunnel can be affected. In the present study, the influence of the porous materials on the boundary layer aerodynamic characteristics is assessed. The consequent aerodynamic noise scattering is also studied, and compared against the acoustic benefit from absorbing reflections in the test section. Geometric modelling is used to understand the influence of varying absorbing materials in reducing the acoustic interference caused by the reflections. The aerodynamic and acoustic results are related to the roughness, and to the viscous and inertial resistivities of the three porous materials studied. The material with highest roughness (polyester wool) is found to result in the strongest turbulent fluctuations in the boundary layer. However, it is the material with the thickest fibre diameter (PU foam), and consequent highest inertial resistivity, which generates the strongest surface noise scattering. Materials with high viscous resistivity, together with low inertial resistivity, are found to provide good sound absorbing capabilities. The results therefore indicate that the best choice of sound absorbing wall treatment for wind tunnel applications results from minimizing roughness and inertial resistivity, while maximizing viscous resistivity.

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In sustainable aviation important aspects are to reduce the greenhouse gas emissions during flight, which have a worldwide impact, and to reduce the noise during take-off and landing. However, in addition airports have an increasing interest in reducing emissions during ground operations to improve the local air quality and potential noise issues. In 2020, Amsterdam Airport Schiphol started a pilot for sustainable taxiing with a pilot-controlled hybrid-electric aircraft-towing vehicle called TaxiBot. During the operational testing, a noise level assessment was performed to evaluate the noise levels to which ground workers are exposed.
For the noise measurements a phased microphone array was used. This allowed for noise source identification through beamforming. In addition, the microphones are used for a noise directionality assessment. Both methods are used to compare the noise sources and levels of a taxibotted versus a conventional taxiing operation. The results show that a taxibotted pass-by produces 7.1 dBA less at a 90deg emission angle at the source position and thus significantly impacts the noise levels on the airport. The directionality assessment shows that the taxibotted operation is especially silent while approaching the observer.@en

In this paper, object-based image analysis classification methods are developed that do not rely on backscatter in order to classify the seafloor. Instead, these methods make use of bathymetry, bathymetric derivatives, and grab samples for classification. The classification is performed on image object statistics. One of the methods utilizes only texture-based features, that is, features that are related to the spatial arrangement of image characteristics. The second method is similar, but relies on a wider set of image object features. The methods were developed and tested using a dataset from Norwegian waters, specifically the Røstbanken area off the coast of Lofoten. The classification results were compared to backscatter-based classification and to grab sample ground-reference data. The algorithm that performed the best was then also applied to a dataset from the Borkumer Stones area close to the island of Schiermonnikoog in Dutch waters. This allowed testing the applicability of the algorithm for different datasets. Because the algorithms that were developed do not require backscatter, the availability of which is much more scarce than bathymetry, and because of the low computational requirements, they could be applied to any area where high-resolution bathymetry and grab samples are available.

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The reduction of aircraft noise over the past decades has generated a growing awareness that the characteristics of a signal can be equally or more important to annoyance than the sound pressure level. Sound can be perceived as more annoying, depending on the frequency content or tonal components. The sound quality metrics loudness, roughness, sharpness, and tonality are important tools to characterize sound. Flyover measurements of landing and takeoff aircraft are investigated in terms of sound quality metrics. The experimental dataset includes 141 measurements of 14 landing aircraft types and 160 measurements of 12 takeoff aircraft types. The sound quality metrics are compared for different aircraft types, and their variability within the same aircraft is investigated. Possible correlations of the sound quality metrics with the airframe, engines, and aircraft operational conditions are investigated. This analysis provides empirical expressions that show a good agreement with experimental data for loudness, sharpness, and roughness for takeoff aircraft. For landing aircraft, empirical expressions could only be obtained for loudness and tonality.

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High-resolution surveying techniques of subtidal soft-bottom seafloor habitats show higher small-scale variation in topography and sediment type than previously thought, but the ecological relevance of this variation remains unclear. In addition, high-resolution surveys of benthic fauna show a large spatial variability in community composition, but this has yet poorly been linked to seafloor morphology and sediment composition. For instance, on soft-bottom coastal shelves, hydrodynamic forces from winds and tidal currents can cause nested multiscale morphological features ranging from metre-scale (mega)ripples, to sand waves and kilometre-scale linear sandbanks. This multiscale habitat heterogeneity is generally disregarded in the ecological assessments of benthic habitats. We therefore developed and tested a novel multiscale assessment toolbox that combines standard bathymetry, multibeam backscatter classification, video surveying of epibenthos and box core samples of sediment and macrobenthos. In a study on the Brown Bank, a sandbank in the southern North Sea, we found that these methods are greatly complementary and allow for more detail in the interpretation of benthic surveys. Acoustic and video data characterised the seafloor surface and subsurface, and macrobenthos communities were found to be structured by both sandbank and sand wave topography. We found indications that acoustic techniques can be used to determine the location of epibenthic reefs. The multiscale assessment toolbox furthermore allows formulating recommendations for conservation management related to the impact of sea floor disturbances through dredging and trawling.

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Realistic predictions of the contribution of the various sources affecting the quality of the bathymetric measurements prior to a survey are of importance to ensure sufficient accuracy of the soundings. To this end, models predicting these contributions have been developed. The objective of the present paper is to assess the performance of the bathymetric uncertainty prediction model for modern Multi-Beam Echo-Sounder (MBES) systems. Two datasets were acquired at water depths of 10m and 30m with three pulse lengths equaling 27 μs, 54 μs, and 134 μs in the Oosterschelde estuary (The Netherlands). The comparison between the bathymetric uncertainties derived from the measurements and those predicted using the current model indicated a relatively good agreement except for the most outer beams. The performance of the uncertainty prediction model improved by accounting for the most recent insights into the contributors to the MBES depth uncertainties, i.e., the Doppler effect, baseline decorrelation (accounting for the pulse shape), and the signal-to-noise ratio.

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The noise emissions of a full-scale nose landing gear (NLG), measured in a wind tunnel and obtained from computational simulations, are compared with those of three regional aircraft types recorded in flyover measurements. A comparison is made with the noise prediction models of Fink, Guo, and German Aerospace Center (DLR). A good agreement was found between all the spectra. The noise emissions up to 1.2 kHz were found to scale with the sixth power of the flow velocity, as usual; however, the spectra at higher frequencies collapsed better when scaled to the seventh power, confirming the fact that high-frequency noise is radiated from the turbulent flow surrounding small features of the NLG. Microphone arrays showed that the main noise sources were located in the middle of the wheel axle. For the flyovers and computational simulations, strong tonal peaks (at around 2200 Hz) were found, which are likely to be caused by open cavities in the NLG. This phenomenon is not accounted for in prediction models. Removing these tones would result in noise reductions of up to 2 dB. Thus, it is highly recommended to include cavity-noise estimations in the current prediction models, or to simply eliminate such cavities where possible with the use of cavity caps.

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