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Noise is considered as an underrated and underemphasized pollutant in contrast to other pollutants of the environment. Due to the non-acute response of health effects, people are not vigilant towards consequences regarding noise pollution. The expansion of the transportation industry is contributing towards the increment in the public and private vehicular volume which causes an increment in noise pollution. For evaluation of respective scenario, the research study has been conducted on one of the minor roads of Nagpur, India; for 2 years, viz., 2012 and 2019. The study concludes an increment of 5–6 dB(A) in noise level, 4–6 times in honking, and 1.7 times in traffic volume. The study confirms increment in sound pressure by 65.9% and 81.9% for the year 2012 and 2019 during morning and evening sessions, respectively. Noise prediction model has also been developed for the abovementioned years, using multiple regression analysis, considering traffic volume, honking, and speed against noise equivalent level. Honking has been further characterized into honk by light and medium category vehicles as acoustical properties of horns vary with respect to category of vehicle and introduced into the noise prediction model. Noise prediction model for 2019 has predicted the noise level in a range of − 1.7 to + 1.4 dB (Leq) with 84% of observations in the range of − 1 to + 1 dB (Leq), when compared with observed Leq on the field. For proper management of noise pollution, a noise prediction model is essentially needed so that the noise level can be anticipated, and accordingly, measures can be outlined and executed. This increased noise level has serious impacts on human hearing capacity and overall health. Accordingly, noise mitigation preventive measures are recommended to control traffic noise in the urban environment.

This paper describes a system noise assessment of a conceptual supersonic aircraft called the NASA 55t Supersonic Technology Concept Aeroplane (STCA), its prediction uncertainty, and related validation tests. A landing and takeoff noise (LTO) standard for supersonic aircraft is needed to realize future supersonic aircraft, and the noise impact due to the introduction of future supersonic aircraft should be analyzed to develop the standard. System noise assessments and uncertainty analyses using Monte Carlo simulation (MCS) were performed. The predicted noise levels showed good agreement with the prior study for both the benchmark case and statistics of the predictions. The predicted cumulative noise level satisfied the ICAO Chapter 4 noise standard, and its standard deviation was approximately 2 EPNdB. Moreover, sensitivity analysis using the obtained datasets revealed strong correlations with the takeoff noise for jet noise, fan exhaust noise at the flyover measurement point, and airframe trailing edge noise. Further understanding of these extracted factors, which were estimated to have a significant impact on the LTO noise, will be beneficial for the development of LTO noise standards and the design of supersonic aircraft.

Noise pollution is one of the negative consequences of growth and development in cities. Traffic noise pollution due to traffic growth is the main aspect that worsens city quality of life. Therefore, research around the world is being conducted to manage and reduce traffic noise. A number of traffic noise prediction models have been proposed employing fixed effect modelling approach considering each observation as independent; however, observations may have spatial and temporal correlations and unobserved heterogeneity. Random effect models overcome these problems. This study attempts to develop a random effect generalized linear model (REGLM) along with a machine learning random forest (RF) model to validate the results, concerning the parameters related to road, traffic and environmental conditions. Models were developed based on the experimental quantities in Delhi in year 2022–2023. Both the models performed comparably well in terms of coefficient of determination. Random forest models with R 2 = 0.75, whereas random effect generalized linear model had an R 2 = 0.70. REGLM model has the ability to quantify the effects of explanatory variables over traffic noise pollution and will be more helpful in prioritizing of resources and chalking out control strategies.

This study attempted to develop a computer-based software for monitoring the traffic noise under heterogeneous traffic condition at the morning peak (MP), off peak (OP), and evening peak (EP) periods of mid-block sections of mid-sized city in India. Traffic noise dataset of 776 ( LA eq , 1hr) were collected from 23 locations of Gorakhpur mid-sized city in the state of Uttar Pradesh in India. K-nearest neighbor (K-NN) algorithm was adopted for traffic noise prediction modeling. Moreover, principal component analysis (PCA) technique was used for the dimensionality reduction and to overcome the problem of multi-collinearity. The developed model exhibits R 2 value of 0.81, 0.78, and 0.77 in the MP, OP, and EP, respectively, for  L eq , and a value of 0.86, 0.80, and 0.84 for L 10 . The proposed model can predict more than 94% observations within an accuracy of ±3%. Ultimately, a user-friendly noise level calculator named “Traffic Noise Prediction Calculator for Heterogeneous Traffic (TNPC-H)” was developed for the benefit of field engineers and policy planners.

Civil aviation noise is one of the main factors hindering the growth of the civil aviation industry. With the increase in global air traffic demand, the problem of aviation noise pollution will become more and more serious. It is of great significance to carry out research in aviation noise. First, by summarizing the characteristics of aviation noise metrics, this paper divides them into three categories: single event noise metrics, cumulative exposure metrics, and daily metrics. Representative metrics of each category are selected for explanation and in-depth analysis. Second, according to the principles of aviation noise prediction models, this paper classifies these existing models into three categories: best practice models, scientific models, and machine learning models. Relevant academic research results are summarized. The best practice model regards the aircraft as noise point source, and its specialty is to predict noise under complex air traffic conditions. The scientific model considers the noise from the level of aircraft components and reflects the underlying physical effects. Based on data, the machine learning model uses algorithms to mine the hidden relationship between various factors and noise to achieve the purpose of noise prediction. Then, this paper introduces two kinds of aviation noise simulation software based on the best practice and scientific models, and lists their access addresses. Finally, challenges and prospects are presented from three aspects: metrics, prediction models and simulation software.

Aircraft noise was among the significant environmental challenges, given increased air traffic affects communities and raises health concerns. Accurate noise prediction is indispensable for designing quieter aircraft and mitigation strategies supporting sustainable aviation practices. In this regard, this study aimed to develop a scalable hybrid machine learning framework to predict aircraft scaled sound pressure levels (SPL). Four models (Extra Tree, AdaBoost, Gradient Boosting, and Histogram-Based Gradient Boosting) were evaluated. The best-performing model is an Extra Tree with an R 2 of 0.9542 and the minimum mean squared error (MSE) of 3.12. Optimization algorithms were performed to improve its accuracy and robustness—JAYA, Enhanced AEO, Levy JAYA, and JADE—which decreased MSE up to 20% for ensuring stable convergence within the first 300 epochs. JAYA and Enhanced AEO had the best results, balancing accuracy and computation efficiency. Optimized models increased the runtime by 20–30% and memory usage by 15%, which makes them fit for offline applications. Under the computational trade-off conditions, the hybrid models revealed a high potential for further improving the accuracy of the noise prediction. The proposed machine learning framework has really given actionable insight into optimizing aircraft design for noise reduction with a minimum loss in their aerodynamic efficiencies. Prediction of the sound pressure level of key parameters, such as the angle of attack, Reynolds number, and surface roughness, contributes to developing effective noise mitigation strategies, including regulatory compliance with sustainability of aviation. The flexibility extends to a range of aircraft components, with quieter and efficient design seriously threatened by environmental and community noise concerns. While the models demonstrated scalability and high accuracy, further refinements are needed to enhance real-time performance and integrate subjective noise metrics, broadening their applicability to diverse aviation noise management scenarios.

The integration of small unmanned aircraft systems (sUASs) and electric vertical takeoff and landing (eVTOL) aircraft into urban airspace presents a new challenge in managing environmental noise, which is a critical factor for the public acceptance of urban air mobility (UAM). This survey investigates the noise characteristics of UAS and eVTOL platforms, particularly multi-rotor and distributed propulsion configurations, and examines whether the operational benefits of these vehicles outweigh their acoustic footprint in dense urban environments. While eVTOLs are often perceived as quieter than conventional helicopters due to the absence of combustion engines and mechanically simpler drivetrains, their dominant noise sources are aerodynamic in nature. These include blade vortex interactions, rotor loading noise, and broadband noise, which persist regardless of whether propulsion is electric or combustion-based. Recent studies suggest that community perception of drone noise is influenced more by tonal content, frequency, and modulation patterns than by absolute sound pressure levels. This paper presents a comprehensive review of state-of-the-art noise prediction tools, empirical measurement techniques, and mitigation strategies for sUAS operating in UAM scenarios. The discussion provided in this paper assists in vehicle design, certification standards, airspace planning, and regulatory frameworks focused on minimizing noise impact in urban settings.

The German Aerospace Center has launched an internal project to assess the noise impact associated with supersonic transport aircraft during approach and departure. A dedicated simulation process is established to cover all relevant disciplines, i.e., aircraft and engine design, engine installation effects, flight simulation, and system noise prediction. The core of the simulation process is comprised of methods at the complexity and fidelity level of conceptual aircraft design, i.e., typical overall aircraft design methods and a semi-empirical approach for the noise modeling. Dedicated interfaces allow to process data from high fidelity simulation that will support or even replace initial low fidelity results in the long run. All of the results shown and discussed in this study are limited to the fidelity level of conceptual design. The application of the simulation process to the NASA 55t Supersonic Technology Concept Aeroplane, i.e., based on non-proprietary data for this vehicle, yields similar noise level predictions when compared to the published NASA results. This is used as an initial feasibility check of the new process and confirms the underlying methods and models. Such an initial verification of the process is understood as an essential step due to the lack of available noise data for supersonic transport aircraft in general. The advantageous effect of engine noise shielding on the resulting system noise is demonstrated based on predicted level time histories and certification noise levels. After this initial verification, the process is applied to evaluate a conceptual supersonic transport design based on a PhD thesis with two engines mounted under the wing, which is referred to as aircraft TWO. Full access to this vehicle’s design and performance data allows to investigate the influence of flight procedures on the resulting noise impact along approach and departure. These noise results are then assembled according to proposed Federal Aviation Agency regulations in their Notice of Proposed Rulemaking, e.g., speed limitations, for Supersonic transport noise certification and the regulations from Noise Chapters of the Annex 16 from the International Civil Aviation Organization in order to evaluate the resulting levels as a function of the flight procedure.

Traffic noise in big cities impacts the people who live and work in high-rise buildings alongside arterial roads. To determine this impact magnitude, this paper proposes and validates a microscopic level method that locally predicts the total noise level and the spectral characteristics of traffic flow in the near-road region. In the proposed method, the vehicles on the road are considered as multiple queues of moving point sound sources with ground reflection considered. To account for the flow of vehicles on the road, traffic field data, and individual vehicle noise source models are also employed. A field measurement is conducted to validate the proposed method. Results comparison shows that the predicted and the measured overall A-weighted sound pressure level and A-weighted noise spectra are within 3 dBA and 5 dBA, respectively. Based on the validated method, the spatial distribution of traffic noise near the arterial road is investigated for different traffic scenarios.

Recent advances in deep neural network (DNN) techniques have increased the importance of security and robustness of algorithms where DNNs are applied. However, several studies have demonstrated that neural networks are vulnerable to adversarial examples, which are generated by adding crafted adversarial noises to the input images. Because the adversarial noises are typically imperceptible to the human eye, it is difficult to defend DNNs. One method of defense is the detection of adversarial examples by analyzing characteristics of input images. Recent studies have used the hidden layer outputs of the target classifier to improve the robustness but need to access the target classifier. Moreover, there is no post-processing step for the detected adversarial examples. They simply discard the detected adversarial images. To resolve this problem, we propose a novel detection-based method, which predicts the adversarial noise and detects the adversarial example based on the predicted noise without any target classification information. We first generated adversarial examples and adversarial noises, which can be obtained from the residual between the original and adversarial example images. Subsequently, we trained the proposed adversarial noise predictor to estimate the adversarial noise image and trained the adversarial detector using the input images and the predicted noises. The proposed framework has the advantage that it is agnostic to the input image modality. Moreover, the predicted noises can be used to reconstruct the detected adversarial examples as the non-adversarial images instead of discarding the detected adversarial examples. We tested our proposed method against the fast gradient sign method (FGSM), basic iterative method (BIM), projected gradient descent (PGD), Deepfool, and Carlini & Wagner adversarial attack methods on the CIFAR-10 and CIFAR-100 datasets provided by the Canadian Institute for Advanced Research (CIFAR). Our method demonstrated significant improvements in detection accuracy when compared to the state-of-the-art methods and resolved the wastage problem of the detected adversarial examples. The proposed method agnostic to the input image modality demonstrated that the noise predictor successfully captured noise in the Fourier domain and improved the performance of the detection task. Moreover, we resolved the post-processing problem of the detected adversarial examples with the reconstruction process using the predicted noise.