Hasil untuk "Acoustics in engineering. Acoustical engineering"

Ermert Cosima A., Schlittmeier Sabine J., Bönsch Andrea et al.

Introduction: Verbal communication depends on a listener’s ability to accurately comprehend and recall information conveyed in a conversation. The heard-text recall (HTR) paradigm can be used in a dual-task design to assess both memory performance and listening effort. The HTR paradigm uses running speech to simulate a conversation between two talkers. Thereby, it allows for talker visualization in virtual reality (VR), providing co-verbal visual cues like lip-movements, turn-taking cues, and gaze behavior. While the HTR in a dual-task design has been investigated under pink noise, the impact of more realistic irrelevant stimuli, such as speech, that provide temporal fluctuations and meaning compared to noise, remains unexplored. Methods: In this study (N = 24), the HTR task as primary task was administered in an immersive VR environment under three noise conditions: silence, pseudo-speech, and speech. Participants performed a vibrotactile secondary task to quantify listening effort in a dual-task design. Results: The results indicate an effect of irrelevant speech on memory and speech comprehension as well as secondary task performance, with a stronger impact of speech relative to pseudo-speech. Discussion: The study validates the sensitivity of the HTR in a dual-task design to background speech stimuli and highlights the relevance of linguistic interference-by-process for listening effort, speech comprehension, and memory.

Ian A. Cosden, Elizabeth Holtz, Joel U. Bretheim

Research Software Engineers (RSEs) have become indispensable to computational research and scholarship. The fast rise of RSEs in higher education and the trend of universities to be slow creating or adopting models for new technology roles means a lack of structured career pathways that recognize technical mastery, scholarly impact, and leadership growth. In response to an immense demand for RSEs at Princeton University, and dedicated funding to grow the RSE group at least two-fold, Princeton was forced to strategize how to cohesively define job descriptions to match the rapid hiring of RSE positions but with enough flexibility to recognize the unique nature of each individual position. This case study describes our design and implementation of a comprehensive RSE career ladder spanning Associate through Principal levels, with parallel team-lead and managerial tracks. We outline the guiding principles, competency framework, Human Resources (HR) alignment, and implementation process, including engagement with external consultants and mapping to a standard job leveling framework utilizing market benchmarks. We share early lessons learned and outcomes including improved hiring efficiency, clearer promotion pathways, and positive reception among staff.

Glorieux Christ, Cops André, Vermeir Gerrit et al.

The Laboratory of Acoustics at KU Leuven was founded more than 50 years ago. Until today, various acoustics-related topics, ranging from physical acoustics through building and room acoustics up to environmental acoustics and noise-related health issues, have been investigated. In the second half of the 20th century, the laboratory was one of the main centres of expertise in acoustics in Belgium, contributing to consulting and establishing legislation in building and environmental acoustics. In the 1990s, it also consolidated expertise in the characterisation of porous materials and was one of the driving research groups in the field of photoacoustics. During the past 15 years, additional research directions have been taken thanks to interdisciplinary collaborations, including psychoacoustics, perception of sound, sound quality assessment, archaeo-acoustics, tackling acoustic issues in building retrofit, and characterisation of walls materials in the framework of sustainable development (recycled materials, biomaterials etc.) This paper first brings a brief historical overview of the past activities of the Laboratory of Acoustics (and Thermal Physics) (ATF), its involvement in national and international collaborations and its main recent scientific and educational activities.

Zhou Zhiyu, Fan Guofan, Zhang Meng et al.

Amid increasing concerns regarding traffic noise pollution, existing research has demonstrated correlations between spatial noise distribution and urban parameters including land use patterns, traffic flow dynamics, and building layouts. Nevertheless, critical gaps persist in establishing actionable frameworks to identify and mitigate high-noise pollution zones during urban planning processes, particularly in high-density cities. This study systematically identifies the spatial attenuation characteristics and determinants of traffic noise in Tianjin through noise map, clustering analysis, correlation analysis, structural equation modelling. The findings revealed that (1) the units with traffic noise spatial attenuation can be classified into four types: attenuation primarily in high noise areas, attenuation primarily in medium noise areas, attenuation primarily in low noise areas, and low total attenuation. (2) The attenuation values of noise in the high and medium noise areas are positively correlated with the ground space index, building boundary density, architectural landscape shape index, and proportion of residential land, and are negatively correlated with the proportion of green space. (3) Through structural equation modelling analysis, this study revealed that the building indicators have a greater effect on the attenuation values of noise in the medium noise areas; the road indicators have a greater effect on the attenuation values of noise in the high noise areas; and the land use indicators have greater effects on high, medium and low noise areas. This study provides evidence-based urban planning strategies for mitigating traffic noise exposure in high-density cities.

Shoichi Koyama, Enzo De Sena, Prasanga Samarasinghe et al.

The study of spatial audio and room acoustics aims to create immersive audio experiences by modeling the physics and psychoacoustics of how sound behaves in space. In the long history of this research area, various key technologies have been developed based both on theoretical advancements and practical innovations. We highlight historical achievements, initiative activities, recent advancements, and future outlooks in the research area of spatial audio recording and reproduction, and room acoustic simulation, modeling, analysis, and control.

Sharon Guardado, Risha Parveen, Zheying Zhang et al.

The integration of Large Language Models (LLMs) in Requirements Engineering (RE) education is reshaping pedagogical approaches, seeking to enhance student engagement and motivation while providing practical tools to support their professional future. This study empirically evaluates the impact of integrating LLMs in RE coursework. We examined how the guided use of LLMs influenced students' learning experiences, and what benefits and challenges they perceived in using LLMs in RE practices. The study collected survey data from 179 students across two RE courses in two universities. LLMs were integrated into coursework through different instructional formats, i.e., individual assignments versus a team-based Agile project. Our findings indicate that LLMs improved students' comprehension of RE concepts, particularly in tasks like requirements elicitation and documentation. However, students raised concerns about LLMs in education, including academic integrity, overreliance on AI, and challenges in integrating AI-generated content into assignments. Students who worked on individual assignments perceived that they benefited more than those who worked on team-based assignments, highlighting the importance of contextual AI integration. This study offers recommendations for the effective integration of LLMs in RE education. It proposes future research directions for balancing AI-assisted learning with critical thinking and collaborative practices in RE courses.

Liyuan Chen, Shuoling Liu, Jiangpeng Yan et al.

The advent of foundation models (FMs), large-scale pre-trained models with strong generalization capabilities, has opened new frontiers for financial engineering. While general-purpose FMs such as GPT-4 and Gemini have demonstrated promising performance in tasks ranging from financial report summarization to sentiment-aware forecasting, many financial applications remain constrained by unique domain requirements such as multimodal reasoning, regulatory compliance, and data privacy. These challenges have spurred the emergence of financial foundation models (FFMs): a new class of models explicitly designed for finance. This survey presents a comprehensive overview of FFMs, with a taxonomy spanning three key modalities: financial language foundation models (FinLFMs), financial time-series foundation models (FinTSFMs), and financial visual-language foundation models (FinVLFMs). We review their architectures, training methodologies, datasets, and real-world applications. Furthermore, we identify critical challenges in data availability, algorithmic scalability, and infrastructure constraints and offer insights into future research opportunities. We hope this survey can serve as both a comprehensive reference for understanding FFMs and a practical roadmap for future innovation.

Davide Venturelli, Erik Gustafson, Doga Kurkcuoglu et al.

We review the prospects to build quantum processors based on superconducting transmons and radiofrequency cavities for testing applications in the NISQ era. We identify engineering opportunities and challenges for implementation of algorithms in simulation, combinatorial optimization, and quantum machine learning in qudit-based quantum computers.

Nazanin Ahmadi, Qianying Cao, Jay D. Humphrey et al.

Physics-informed machine learning (PIML) is emerging as a potentially transformative paradigm for modeling complex biomedical systems by integrating parameterized physical laws with data-driven methods. Here, we review three main classes of PIML frameworks: physics-informed neural networks (PINNs), neural ordinary differential equations (NODEs), and neural operators (NOs), highlighting their growing role in biomedical science and engineering. We begin with PINNs, which embed governing equations into deep learning models and have been successfully applied to biosolid and biofluid mechanics, mechanobiology, and medical imaging among other areas. We then review NODEs, which offer continuous-time modeling, especially suited to dynamic physiological systems, pharmacokinetics, and cell signaling. Finally, we discuss deep NOs as powerful tools for learning mappings between function spaces, enabling efficient simulations across multiscale and spatially heterogeneous biological domains. Throughout, we emphasize applications where physical interpretability, data scarcity, or system complexity make conventional black-box learning insufficient. We conclude by identifying open challenges and future directions for advancing PIML in biomedical science and engineering, including issues of uncertainty quantification, generalization, and integration of PIML and large language models.

Fabian C. Peña

Large Language Models (LLMs) are revolutionizing software engineering (SE), with special emphasis on code generation and analysis. However, their applications to broader SE practices including conceptualization, design, and other non-code tasks, remain partially underexplored. This research aims to augment the generality and performance of LLMs for SE by (1) advancing the understanding of how LLMs with different characteristics perform on various non-code tasks, (2) evaluating them as sources of foundational knowledge in SE, and (3) effectively detecting hallucinations on SE statements. The expected contributions include a variety of LLMs trained and evaluated on domain-specific datasets, new benchmarks on foundational knowledge in SE, and methods for detecting hallucinations. Initial results in terms of performance improvements on various non-code tasks are promising.

Theyssen Jannik

The noise of railway wheels is one of the main contributors to railway rolling noise. Auralization, the rendering of sound fields from virtual sources, is a promising tool for studying rolling noise, as it enables the study of perceptual qualities of noise. Generating such sound fields based on physical models requires knowledge of the structural vibrations and radiation characteristics of the wheels. The vibration and radiation of a railway wheel are typically dominated by highly undamped modes. The amplitudes of the various modes depend on the roughness excitation and the contact position of the wheel on the rail. For auralization, it is relevant to investigate which modes are significant in reproducing the equivalent sound pressure level (SPL), as well as psychoacoustic quantities. Identifying significant modes can also help simplify the physical model. This article explores the influence of lateral contact positions on wheel radiation and analyzes the modal contributions to pass-by SPLs. Using a timedomain prediction model for the sound pressure produced by one wheel as it passes a stationary track side position, the psychoacoustic quantities loudness and sharpness were investigated. The smallest number of modes required to reproduce equivalent pressure levels and psychoacoustic quantities is identified for two contact positions. For simplicity, the discussion is limited to one wheel, surface roughness, and vehicle speed. The results show possible simplifications in auralization models and can enable noise mitigation with a focus on psychoacoustic parameters.

Daniel R. Clarkson, Lawrence A. Bull, Chandula T. Wickramarachchi et al.

Regression is a fundamental prediction task common in data-centric engineering applications that involves learning mappings between continuous variables. In many engineering applications (e.g.\ structural health monitoring), feature-label pairs used to learn such mappings are of limited availability which hinders the effectiveness of traditional supervised machine learning approaches. The current paper proposes a methodology for overcoming the issue of data scarcity by combining active learning with hierarchical Bayesian modelling. Active learning is an approach for preferentially acquiring feature-label pairs in a resource-efficient manner. In particular, the current work adopts a risk-informed approach that leverages contextual information associated with regression-based engineering decision-making tasks (e.g.\ inspection and maintenance). Hierarchical Bayesian modelling allow multiple related regression tasks to be learned over a population, capturing local and global effects. The information sharing facilitated by this modelling approach means that information acquired for one engineering system can improve predictive performance across the population. The proposed methodology is demonstrated using an experimental case study. Specifically, multiple regressions are performed over a population of machining tools, where the quantity of interest is the surface roughness of the workpieces. An inspection and maintenance decision process is defined using these regression tasks which is in turn used to construct the active-learning algorithm. The novel methodology proposed is benchmarked against an uninformed approach to label acquisition and independent modelling of the regression tasks. It is shown that the proposed approach has superior performance in terms of expected cost -- maintaining predictive performance while reducing the number of inspections required.

Abram H. Clark, Derek R. Olson, Andrew J. Swartz et al.

Here we theoretically and computationally study the frequency dependence of phase speed and attenuation for marine sediments from the perspective of granular mechanics. We leverage recent theoretical insights from the granular physics community as well as discrete-element method simulations, where the granular material is treated as a packing of discrete objects that interact via pairwise forces. These pairwise forces include both repulsive contact forces as well as dissipative terms which may include losses from the fluid as well as losses from inelasticity at grain-grain contacts. We show that the structure of disordered granular packings leads to anomalous scaling laws for frequency-dependent phase speed and attenuation that do not follow from a continuum treatment. Our results demonstrate that granular packing structure, which is not explicitly considered in existing models, may play a crucial role in a complete theory of sediment acoustics. While this simple approach does not explicitly treat sound propagation or inertial effects in the interstitial fluid, it provides a starting point for future models that include these and other more complex features.

Ronald N. Miles

Nicol Rozenn, Monfort Jean-Yves

In its early age, telecommunication was focused on voice communications, and acoustics was at the heart of the work related to speech coding and transmission, automatic speech recognition or speech synthesis, aiming at offering better quality (Quality of Experience or QoE) and enhanced services to users. As technology has evolved, the research themes have diversified, but acoustics remains essential. This paper gives an overview of the evolution of acoustic research for telecommunication. Communication was initially (and for a long time) only audio with a monophonic narrow-band sound (i.e. [300–3400 Hz]). After the bandwidth extension (from the wide-band [100–7000 Hz] to the full-band [20 Hz–20 kHz] range), a new break was the introduction of 3D sound, either to provide telepresence in audioconferencing or videoconferencing, or to enhance the QoE of contents such as radio, television, VOD, or video games. Loudspeaker or microphone arrays have been deployed to implement “Holophonic” or “Ambisonic” systems. The interaction between spatialized sounds and 3D images was also investigated. At the end of the 2000s, smartphones invaded our lives. Binaural sound was immediately acknowledged as the most suitable technology for reproducing 3D audio on smartphones. However, to achieve a satisfactory QoE, binaural filters need to be customized in relation with the listener’s morphology. This question is the main obstacle to a mass-market distribution of binaural sound, and its solving has prompted a large amount of work. In parallel with the development of technologies, their perceptual evaluation was an equally important area of research. In addition to conventional methods, innovative approaches have been explored for the assessment of sound spatialization, such as physiological measurement, neuroscience tools or Virtual Reality (VR). The latest development is the use of acoustics as a universal sensor for the Internet of Things (IoT) and connected environments. Microphones can be deployed, preferably with parcimony, in order to monitor surrounding sounds, with the goal of detecting information or events thanks to models of automatic sound recognition based on neural networks. Applications range from security and personal assistance to acoustic measurement of biodiversity. As for the control of environments or objects, voice commands have become widespread in recent years thanks to the tremendous progress made in speech recognition, but an even more intuitive mode based on direct control by the mind is proposed by Brain Computer Interfaces (BCIs), which rely on sensory stimulation using different modalities, among which the auditory one offers some advantages.

Fargeot Simon, Vidal Adrien, Aramaki Mitsuko et al.

This paper presents a perceptual experiment aimed at assessing the spatial quality of acoustic environment rendering using a 4th order ambisonic auralization system. A novel test protocol is developed for this purpose, based on comparing the perceived spatial attributes of sound sources in both real (in-situ) and virtual listening conditions (loudspeaker-based ambisonic auralization of measured SRIRs). The perceptual evaluation is conducted using a specific reporting method combined with a virtual reality interface, enabling simultaneous assessment of perceived distance, angular position, and apparent width of sound sources. The test is conducted in three “office like” rooms, varying in reverberation properties and size. The results highlight differences in spatial perception between (a) real rooms and (b) their reproduction through the auralization system. Overall, localization performance is worse in auralized conditions than in real conditions, as evidenced by a clear increase in localization errors in azimuth and elevation, along with an increase in reported source width. This study also reveals that the spatial accuracy of the auralization depends on the rooms being auralized.

Cretagne Léo, Garcia A. Carlos, Leconte Roman et al.

The renewal of civil supersonic aviation is partly conditioned by the establishment of an international regulation on sonic boom level. Human perception of booms from future aircraft creating sound disturbances of lower level than past ones can currently be evaluated only through boom simulators in laboratory setups with predicted signatures from numerical simulations. To reach sufficient ecological validity, it is necessary that perception studies take place in an environment as familiar as possible to participants. With this in view, a simulator has been designed to reproduce sonic booms of low amplitude with the highest possible fidelity and control, while adapting to an existing house. The article presents the challenges and design solutions chosen to reach this objective. A double optimisation of the input signal, successively in the frequency and in the time domain, is described. Observed performances are presented for different boom exposures and in various rooms of the house.

Kaltenbacher Manfred, Kergomard Jean, Gaborit Mathieu et al.

Postma Barteld N.J., Katz Brian F.G.

Previous studies have discussed six pre-Sabine quantifiable guidelines employed in room acoustic design: voice directivity, audience rake, “echo theory”, stage acoustics, reverberation, and length, width, and height ratios. Around the turn of the 18th century, these notions led to two shapes that were theoretically regarded optimal for rooms with acoustical demands: ellipse and semi-circle. The first of these shapes to be tested was the ellipse in the design for the Iffland Theatre (1802–1817). As the resulting acoustics were notoriously poor, contemporary architects and acousticians discussed the grounds for the failed acoustics as well as possible corrections. Multiple subsequent halls were also based on lessons learned from this acoustic failure. As part of this archaeoacoustics research, geometric acoustic numerical simulations were employed to estimate the actual and renovated room acoustic conditions. Three configurations of the hall have been reconstructed. Results show that the hall’s shape led to sound focusing and that the rounded proscenium arch likely induced echoes. Proposed solutions of the time to increase the scattering or absorption appear unlikely to have solved the observed acoustic problems.

Dongwei Xiao, Zhibo Liu, Shuai Wang

A physical simulation engine (PSE) is a software system that simulates physical environments and objects. Modern PSEs feature both forward and backward simulations, where the forward phase predicts the behavior of a simulated system, and the backward phase provides gradients (guidance) for learning-based control tasks, such as a robot arm learning to fetch items. This way, modern PSEs show promising support for learning-based control methods. To date, PSEs have been largely used in various high-profitable, commercial applications, such as games, movies, virtual reality (VR), and robotics. Despite the prosperous development and usage of PSEs by academia and industrial manufacturers such as Google and NVIDIA, PSEs may produce incorrect simulations, which may lead to negative results, from poor user experience in entertainment to accidents in robotics-involved manufacturing and surgical operations. This paper introduces PHYFU, a fuzzing framework designed specifically for PSEs to uncover errors in both forward and backward simulation phases. PHYFU mutates initial states and asserts if the PSE under test behaves consistently with respect to basic Physics Laws (PLs). We further use feedback-driven test input scheduling to guide and accelerate the search for errors. Our study of four PSEs covers mainstream industrial vendors (Google and NVIDIA) as well as academic products. We successfully uncover over 5K error-triggering inputs that generate incorrect simulation results spanning across the whole software stack of PSEs.