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 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.

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.

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.

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.

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/).

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.

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.

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.

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.