Hasil untuk "Acoustics in engineering. Acoustical engineering"

Mali Harshada, Nisha Kavassery Venkateswaran

Auditory spatial discrimination measures, including interaural time difference (ITD), interaural level difference (ILD), minimum audible angle (MAA) and bisection accuracy (BA), are key components of spatial auditory processing and contribute to accurate sound localization. Age-related declines in these abilities can negatively impact spatial awareness and daily communication. The study investigated the sensitivity of four spatial hearing measures – ITD, ILD, MAA and BA to age-related changes in auditory spatial discrimination. An experimental cross-sectional study design was adopted, with purposive sampling of 44 clinically normal-hearing participants (22 young and 22 middle-aged adults). Spatialized white noise bursts were generated by convolving signals with non-individualized head-related transfer functions using the 3D Tune-In Toolkit, a software environment for simulating 3D audio over headphones. ITD and ILD tasks involved detecting time and intensity differences between ears. MAA assessed the smallest discriminable angle. BA measured the ability to bisect auditory space into two hemifields. MANOVA revealed significant main effect of age across all measures (p <  0.001), with middle-aged adults showing significantly poorer spatial discrimination. Receiver Operating Characteristic analyses identified MAA as the most sensitive measure. Fisher’s Discriminant analysis further validated the discriminatory power of MAA for group categorization. These findings suggest that auditory spatial discrimination abilities may begin to show subtle changes at mid-adulthood. The MAA shows to be a promising marker out of the four measures for detecting early spatial processing deficits.

Kappis Achilles, Cheer Jordan

Virtual sensing methods are utilised in active noise control systems where the error sensors cannot be placed at the locations where control is physically required. Their performance critically depends on the spatial configuration of the physical monitoring microphones used to estimate the pressures at the virtual error sensor locations. This paper investigates the use of a genetic algorithm to calculate optimal microphone placements for estimation within a stationary diffuse sound field. A multi-objective optimisation framework is formulated, simultaneously minimising the estimation error and the condition number of the monitoring microphone power spectral density matrix, thereby addressing both estimation accuracy and robustness to uncertainties. Optimisations are carried out for a single frequency and for three representative frequencies spanning three octaves. The resulting Pareto fronts reveal the inherent trade-off between performance and numerical stability. The Technique for Order of Preference by Similarity to Ideal Solution is applied to select a single optimal solution from each Pareto set. These solutions achieve a balanced compromise, offering a small reduction in estimation performance while reducing the condition number by up to an order of magnitude compared with configurations that solely minimise the error. The minimum error and optimal solutions are evaluated over a broad frequency range, where the optimal designs are shown to significantly reduce the conditioning for a modest increase in estimation errors. The study highlights characteristic spatial patterns that promote optimal performance, and demonstrates the effectiveness of a genetic algorithm-based multi-objective optimisation for designing robust microphone configurations for virtual sensing applications.

T. V. Waterschoot

Our everyday auditory experience is shaped by the acoustics of the indoor environments in which we live. Room acoustics modeling is aimed at establishing mathematical representations of acoustic wave propagation in such environments. These representations are relevant to a variety of problems ranging from echo-aided auditory indoor navigation to restoring speech understanding in cocktail party scenarios. Many disciplines in science and engineering have recently witnessed a paradigm shift powered by deep learning (DL), and room acoustics research is no exception. The majority of deep, data-driven room acoustics models are inspired by DL-based speech and image processing, and hence lack the intrinsic space-time structure of acoustic wave propagation. More recently, DL-based models for room acoustics that include either geometric or wave-based information have delivered promising results, primarily for the problem of sound field reconstruction. In this review paper, we will provide an extensive and structured literature review on deep, data-driven modeling in room acoustics. Moreover, we position these models in a framework that allows for a conceptual comparison with traditional physical and data-driven models. Finally, we identify strengths and shortcomings of deep, data-driven room acoustics models and outline the main challenges for further research.

Ryan A. McCarthy, You Zhang, Samuel A. Verburg et al.

Acoustic data provide scientific and engineering insights in fields ranging from bioacoustics and communications to ocean and earth sciences. In this review, we survey recent advances and the transformative potential of machine learning (ML) in acoustics including deep learning (DL). Using the Python high-level programming language, we demonstrate a broad collection of ML techniques to detect and find patterns for classification, regression, and generation in acoustics data automatically. We have ML examples including acoustic data classification, generative modeling for spatial audio, and physics-informed neural networks. This work includes AcousticsML, a set of practical Jupyter notebook examples on GitHub demonstrating ML benefits and encouraging researchers and practitioners to apply reproducible data-driven approaches to acoustic challenges.

J. Wilk-Jakubowski, L. Pawlik, Damian Frej et al.

The increasing demands for the reliability of modern industrial equipment and structures necessitate advanced techniques for design, monitoring, and analysis. This review article presents the latest research advancements in the application of machine learning techniques to vibration and acoustic signal analysis from 2015 to 2024. A total of 96 peer-reviewed scientific publications were examined, selected using a systematic Scopus-based search. The main research areas include processes such as modeling and design, health management, condition monitoring, non-destructive testing, damage detection, and diagnostics. In the context of these processes, a review of machine learning techniques was conducted, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), autoencoders, support vector machines (SVMs), decision trees (DTs), nearest neighbor search (NNS), K-means clustering, and random forests. These techniques were applied across a wide range of engineering domains, including civil infrastructure, transportation systems, energy installations, and rotating machinery. Additionally, this article analyzes contributions from different countries, highlighting temporal and methodological trends in this field. The findings indicate a clear shift towards deep learning-based methods and multisensor data fusion, accompanied by increasing use of automatic feature extraction and interest in transfer learning, few-shot learning, and unsupervised approaches. This review aims to provide a comprehensive understanding of the current state and future directions of machine learning applications in vibration and acoustics, outlining the field’s evolution and identifying its key research challenges and innovation trajectories.

Mengyao Wang, Jiaye Li, Ji Chen et al.

Alessandro Zanarini

This study aims to proficiently characterise the direct and inverse vibro-acoustics of radiating plates by means of experiment-based full-field approaches instead of hard-to-tune structural simulations. Dynamic airborne pressure fields, in any vehicle engineering, can severely excite lightweight structures and components, becoming a concern for their structural integrity and reliability. The final aim is to identify, once the airborne pressure field is known in its spectrum, the broad frequency band force that is transmitted to the excitation points used in the direct vibro-acoustic FRF problem. The Rayleigh integral approximation of sound radiation from a vibrating surface is here revisited in early attempts of direct and inverse vibro-acoustics, exploiting complex-valued experiment-based full-field receptances, without the inertia-related distortions of traditional measurement transducers, in a receptance-based approach that does not need the model updating steps. Details and considerations up to the inverse full-field formulation, with special attention to its complex-valued nature and broad excitation dynamic signature, together with examples coming from a real thin plate tested, are thoroughly provided in this work. This study demonstrates what is nowadays achievable in experiment-based vibro-acoustics by means of moderate density DIC-based measurements. DIC, within its domain of applicability, can be used successfully to predict the radiated sound and to retrieve the forces induced by sound-pressure fields.

Zhao Qingsong, Wang Xiufeng, Luo Kun et al.

For a long time, the traditional motor manufacturing industry relies on the artificial hearing method to identify whether there is abnormal noise in the motor, thus leading to low efficiency and poor accuracy consistency. To solve these problems, a new prediction method based on the algorithm of harmonic percussion sound separation (HPSS) and continuous interphase sampling (CIS) of cochlear implants and the CNN-CBAM (Convolutional neural network based on Convolutional Block Attention Module) model, is proposed in this paper. Firstly, the original sound signals are separated into harmonic and percussive components by the HPSS algorithm, and then each component is processed by the CIS algorithm of cochlear implant to obtain electrode stimulation signal that can simulate human hearing. Subsequently, the classification task of motors are achieved by a deep learning model that combines CNN and CBAM. The proposed method is verified that the highest accuracy of 99.27% is achieved in the motor data set. Afterward for feature extraction, the results of ablation experiments with HPSS-CIS show that the average accuracy of this method is more than 4.5% higher than that of any single component. In addition, for the human auditory feature extraction method after HPSS processing, the CIS method is compared with the widely used Mel filter bank, and shows better performance.

Gallo Erica, De Decker Julien, Bresciani Andrea et al.

This paper describes the design and commissioning of an aeroacoustic test rig for the study of single and coaxial propeller propulsive systems. The size of the propellers matches typical drone applications. The experimental setup, designed and commissioned at the ALCOVES anechoic laboratory of von Karman Institute for Fluid Dynamics, is equipped with aerodynamic sensors for performance analysis and is surrounded by a microphone antenna for the characterization of the noise level and directivity. Thefacility permits varying different parameters such as the longitudinal distance between the rotor planes, and the rotational speed/direction of each propeller. Requirements for the qualification of the test room consist of low-level background noise and minimized turbulence ingestion noise. Two experimental databases have been constituted and are joined to the present paper: (i) for the DJI 9450 two-bladed propeller, verified against data from the literature, and (ii) for single and coaxial contra-rotating Mejzlik two-bladed propellers. The proposed benchmark data will support the validation of low- and high-fidelity numerical methods.

Gazengel Bruno, Dalmont Jean-Pierre, Gaillard Amélie et al.

This paper describes an experimental measurement setup for the characterization of single-reed mouthpiece interaction under quasi-static conditions. Measurement leads to the estimation of the nonlinear characteristics, establishing the relation between pressure drop across the reed channel and the jet cross-section. Measurements with various lip forces show that the resultant nonlinear characteristics can be described by a single nonlinear characteristic linking the generalized pressure and the jet cross-section. This generalized pressure is the sum of the pressure drop and the lip pressure, defined as the lip force divided by an equivalent lip surface determined for each reed. The nonlinear characteristic is then modeled as a function depending on three parameters: the opening at rest, the linear stiffness for low pressures, and the “elbow pressure,” which allows to make the link between the two affine parts of the function. The characterization of 24 tenor saxophone reeds shows that the model fits the experimental characteristics with an inaccuracy that can be considered as a supplementary parameter for the reed. Finally, the reeds can be characterized with with only five parameters, the inaccuracy, the lip equivalent surface and the three parameters of the nonlinear model. First results suggest that equivalent lip surface and inaccuracy depends on the reed type while reed opening at rest and linear stiffness depends on the reed.

Bellows Samuel D., Avila Joseph E., Leishman Timothy W.

The directional radiation patterns of musical instruments have long been defining characteristics known to influence their perceived qualities. Technical understanding of musical instrument directivities is essential for applications such as concert hall design, auralizations, and recording microphone placements. Nonetheless, the difficulties in measuring sound radiation from musician-played instruments at numerous locations over a sphere have severely limited their directivity measurement resolutions compared to standardized loudspeaker resolutions. This work illustrates how a carefully implemented multiple-capture transfer-function method adapts well to played musical instrument directivities and achieves compatible resolutions. Comparisons between a musician-played and artificially excited trumpet attached to a mannikin validate the approach’s effectiveness. The results demonstrate the trumpet’s highly directional characteristics at high frequencies and underscore the crucial effects of musician diffraction. Spherical spectral analysis reveals that standardized resolutions may only be sufficient to produce valid complex-valued directivities up to nearly 4 kHz, emphasizing the need for high-resolution, played musical instrumentdirectivity measurements.

Jay Lee, Hanqi Su, Dai-Yan Ji et al.

Over the past ten years, the application of artificial intelligence (AI) and machine learning (ML) in engineering domains has gained significant popularity, showcasing their potential in data-driven contexts. However, the complexity and diversity of engineering problems often require the development of domain-specific AI approaches, which are frequently hindered by a lack of systematic methodologies, scalability, and robustness during the development process. To address this gap, this paper introduces the "ABCDE" as the key elements of Engineering AI and proposes a unified, systematic engineering AI ecosystem framework, including eight essential layers, along with attributes, goals, and applications, to guide the development and deployment of AI solutions for specific engineering needs. Additionally, key challenges are examined, and eight future research directions are highlighted. By providing a comprehensive perspective, this paper aims to advance the strategic implementation of AI, fostering the development of next-generation engineering AI solutions.

Anatoly A. Krasnovsky

Chaos engineering reveals resilience risks but is expensive and operationally risky to run broadly and often. Model-based analyses can estimate dependability, yet in practice they are tricky to build and keep current because models are typically handcrafted. We claim that a simple connectivity-only topological model - just the service-dependency graph plus replica counts - can provide fast, low-risk availability estimates under fail-stop faults. To make this claim practical without hand-built models, we introduce model discovery: an automated step that can run in CI/CD or as an observability-platform capability, synthesizing an explicit, analyzable model from artifacts teams already have (e.g., distributed traces, service-mesh telemetry, configs/manifests) - providing an accessible gateway for teams to begin resilience testing. As a proof by instance on the DeathStarBench Social Network, we extract the dependency graph from Jaeger and estimate availability across two deployment modes and five failure rates. The discovered model closely tracks live fault-injection results; with replication, median error at mid-range failure rates is near zero, while no-replication shows signed biases consistent with excluded mechanisms. These results create two opportunities: first, to triage and reduce the scope of expensive chaos experiments in advance, and second, to generate real-time signals on the system's resilience posture as its topology evolves, preserving live validation for the most critical or ambiguous scenarios.

Georgi Markov, Jon G. Hall, Lucia Rapanotti

Many organisational problems are addressed through systemic change and re-engineering of existing Information Systems rather than radical new design. In the face of widespread IT project failure, devising effective ways to tackle this type of change remains an open challenge. This work discusses the motivation, theoretical foundation, characteristics and evaluation of a novel framework - referred to as POE-$Δ$, which is rooted in design and engineering and is aimed at providing systematic support for representing, structuring and exploring change problems of a socio-technical nature, including implementing their solutions when they exist. We generalise an existing framework of greenfield design as problem solving for application to change problems. From a theoretical perspective,POE-$Δ$ is a strict extension to its parent framework, allowing the seamless integration of greenfield and brownfield design to tackle change problems. A Design Science Research methodology was applied over a decade to define and evaluate POE-$Δ$, with significant case study research conducted to evaluate the framework in its application to real-world change problems of varying criticality and complexity. The results show that POE-$Δ$ exhibits desirable characteristics of a design approach to organisational change and can bring tangible benefits when applied in practice as a holistic and systematic approach to change in socio-technical contexts.

X. Ji, Q. Liu, D. H. Chen et al.

T. Fava, A. Cavalieri

This study explores the use of parabolized stability equations (PSEs) for predicting sound propagation in ducts, a novel application in computational duct acoustics. The PSE, formulated in a general duct-fitted coordinate system, was validated against several test cases, including uniform flow, axial temperature gradients, and laminar/turbulent flows, demonstrating close agreement with existing literature. This paper highlights limitations of the PSE, particularly when the local Helmholtz number decreases, potentially causing mode cutoff, and suggests remedies for mitigating phase and amplitude errors. The efficiency of the PSE is further demonstrated through a comparison with linearized Euler equations for forward fan noise propagation in a turbofan inlet, showing 75.8% reduced computational time and 98.2% reduced memory usage. These computational advantages become more significant as problem size increases, with the PSE outperforming traditional finite element and parabolic approximation methods, especially in cases involving viscous shear flow effects. This makes the PSE particularly well-suited for applications such as boundary layer shielding and liner-boundary layer interactions. The study provides a promising avenue for future acoustic research and practical engineering applications, emphasizing the efficiency and accuracy of PSE in complex duct acoustics.

Balogun Adedotun Q Balogun Adedotun Q, Ogunnaike Adekunle O Ogunnaike Adekunle O, B. Adekunle, S.

Post-production studios (mixing/editing rooms) require precise acoustic control to ensure sound fidelity and user comfort. Traditional acoustic treatments (bass traps, absorptive panels, diffusers) often face challenges such as bulky installations and limited low-frequency absorption. Recently, acoustic metamaterials engineered, subwavelengthstructured materials have emerged as innovative solutions. These materials exhibit unusual acoustic properties (e.g. negative effective density, resonant absorption) that enable strong low-frequency attenuation in thin panels. This paper reviews current research on metamaterials relevant to studio acoustics, analyzes design considerations (room geometry, diffusion, absorption) in post-production environments, and proposes integrated solutions. We build upon a case study thesis of a Lagos film studio and global literature on metamaterials. The analysis discusses acoustic challenges (reverberation control, isolation, speech clarity) in studio contexts and how metamaterial-based absorbers/diffusers can address them. A methodology combining literature review with acoustic modeling is described. In the Results/Discussion, we include a comparative table linking common acoustic problems to metamaterial solutions, supported by recent studies. The paper concludes that integrating metamaterials with optimized room geometry can significantly improve studio performance, though practical implementation (fabrication and installation) remains an active research area. acoustics; User comfort; University lecture halls; Nigeria; Acoustic design; Higher education architecture.

Yasushi Horii, Akari Ide, Toshiaki Kitamura

In this study, a straight cochlear model with a symmetric structure was utilized to elucidate the mechanism of traveling wave excitation. It was demonstrated that even-and odd-symmetric sound wave components are generated within the cochlea, and the cochlear acoustic phenomena can be described as their superposition. Furthermore, it was reported that the odd-symmetric sound wave component couples with the traveling wave, propagating through the cochlea at the same speed as the traveling wave while transferring energy to it.Clinical Relevance— A highly versatile cochlear model capable of detailed analysis of cochlear mechanisms has been developed. As it can be applied not only to cochlear physiology but also to acoustics-based auditory disorders — such as perilymph fistula, round window atresia, pneumolabyrinth, and Meniere’s disease — this model offers significant potential for widespread use in auditory healthcare.

Thamasha Samarasinghe, Sumudu Herath

This study investigates the acoustic performance of various porous materials at elevated temperatures, employing Constrained Gaussian Process Regression (CGPR) to model the relationship between specific airflow resistance and the absolute temperature. Experimental data on six fibrous material samples at 600°C are used to develop and compare CGPR models against conventional Power Law Regression (PLR) methods. The developed CGPR incorporates constraints such as boundedness, monotonicity and convexity/concavity derived from the evident relationships and prior knowledge of the specific airflow resistance versus temperature variations. The results of this study prove the outperformance of the developed CGPR over PLR methods in terms of data efficiency, predictive accuracy, uncertainty quantification, overfitting recovery and extrapolation capability. This comparative analysis outlines a significant improvement in the predictive accuracy of CGPR, achieving improved coefficient of determination values compared to PLR. CGPR also furnishes a direct strategy to quantify the uncertainty of predictions which is vital for applications at elevated temperatures. Additionally, CGPR offers valuable insights into sound absorption behaviour, highlighting its applicability in thermal acoustics and materials engineering. Prospective research avenues stem from this research as the developed CGPR technique has the potential to replace various modelling techniques in materials science and acoustic engineering applications.

Shirui Wang, Yixin Zhao, Yimeng Song et al.