Hasil untuk "Acoustics in engineering. Acoustical engineering"

Chaoyue Niu, Veronica Rowe, Guy J. Brown et al.

Paediatric obstructive sleep apnoea (OSA) is clinically significant yet difficult to diagnose, as children poorly tolerate sensor-based polysomnography. Acoustic monitoring provides a non-invasive alternative for home-based OSA screening, but limited paediatric data hinders the development of robust deep learning approaches. This paper proposes a transfer learning framework that adapts acoustic models pretrained on adult sleep data to paediatric OSA detection, incorporating SpO2-based desaturation patterns to enhance model training. Using a large adult sleep dataset (157 nights) and a smaller paediatric dataset (15 nights), we systematically evaluate (i) single- versus multi-task learning, (ii) encoder freezing versus full fine-tuning, and (iii) the impact of delaying SpO2 labels to better align them with the acoustics and capture physiologically meaningful features. Results show that fine-tuning with SpO2 integration consistently improves paediatric OSA detection compared with baseline models without adaptation. These findings demonstrate the feasibility of transfer learning for home-based OSA screening in children and offer its potential clinical value for early diagnosis.

Hao Li, Haoxiang Zhang, Ahmed E. Hassan

The future of software engineering--SE 3.0--is unfolding with the rise of AI teammates: autonomous, goal-driven systems collaborating with human developers. Among these, autonomous coding agents are especially transformative, now actively initiating, reviewing, and evolving code at scale. This paper introduces AIDev, the first large-scale dataset capturing how such agents operate in the wild. Spanning over 456,000 pull requests by five leading agents--OpenAI Codex, Devin, GitHub Copilot, Cursor, and Claude Code--across 61,000 repositories and 47,000 developers, AIDev provides an unprecedented empirical foundation for studying autonomous teammates in software development. Unlike prior work that has largely theorized the rise of AI-native software engineering, AIDev offers structured, open data to support research in benchmarking, agent readiness, optimization, collaboration modeling, and AI governance. The dataset includes rich metadata on PRs, authorship, review timelines, code changes, and integration outcomes--enabling exploration beyond synthetic benchmarks like SWE-bench. For instance, although agents often outperform humans in speed, their PRs are accepted less frequently, revealing a trust and utility gap. Furthermore, while agents accelerate code submission--one developer submitted as many PRs in three days as they had in three years--these are structurally simpler (via code complexity metrics). We envision AIDev as a living resource: extensible, analyzable, and ready for the SE and AI communities. Grounding SE 3.0 in real-world evidence, AIDev enables a new generation of research into AI-native workflows and supports building the next wave of symbiotic human-AI collaboration. The dataset is publicly available at https://github.com/SAILResearch/AI_Teammates_in_SE3. > AI Agent, Agentic AI, Coding Agent, Agentic Coding, Software Engineering Agent

Armin Ariamajd, Raquel López-Ríos de Castro, Andrea Volkamer

The increasing importance of Computational Science and Engineering has highlighted the need for high-quality scientific software. However, research software development is often hindered by limited funding, time, staffing, and technical resources. To address these challenges, we introduce PyPackIT, a cloud-based automation tool designed to streamline research software engineering in accordance with FAIR (Findable, Accessible, Interoperable, and Reusable) and Open Science principles. PyPackIT is a user-friendly, ready-to-use software that enables scientists to focus on the scientific aspects of their projects while automating repetitive tasks and enforcing best practices throughout the software development life cycle. Using modern Continuous software engineering and DevOps methodologies, PyPackIT offers a robust project infrastructure including a build-ready Python package skeleton, a fully operational documentation and test suite, and a control center for dynamic project management and customization. PyPackIT integrates seamlessly with GitHub's version control system, issue tracker, and pull-based model to establish a fully-automated software development workflow. Exploiting GitHub Actions, PyPackIT provides a cloud-native Agile development environment using containerization, Configuration-as-Code, and Continuous Integration, Deployment, Testing, Refactoring, and Maintenance pipelines. PyPackIT is an open-source software suite that seamlessly integrates with both new and existing projects via a public GitHub repository template at https://github.com/repodynamics/pypackit.

D. Veerababu, Prasanta K. Ghosh

Neural networks constrained by the physical laws emerged as an alternate numerical tool. In this paper, the governing equation that represents the propagation of sound inside a one-dimensional duct carrying a heterogeneous medium is derived. The problem is converted into an unconstrained optimization problem and solved using neural networks. Both the acoustic state variables: acoustic pressure and particle velocity are predicted and validated with the traditional Runge-Kutta solver. The effect of the temperature gradient on the acoustic field is studied. Utilization of machine learning techniques such as transfer learning and automatic differentiation for acoustic applications is demonstrated.

Leonhard Applis, Yuntong Zhang, Shanchao Liang et al.

The growth of Large Language Model (LLM) technology has raised expectations for automated coding. However, software engineering is more than coding and is concerned with activities including maintenance and evolution of a project. In this context, the concept of LLM agents has gained traction, which utilize LLMs as reasoning engines to invoke external tools autonomously. But is an LLM agent the same as an AI software engineer? In this paper, we seek to understand this question by developing a Unified Software Engineering agent or USEagent. Unlike existing work which builds specialized agents for specific software tasks such as testing, debugging, and repair, our goal is to build a unified agent which can orchestrate and handle multiple capabilities. This gives the agent the promise of handling complex scenarios in software development such as fixing an incomplete patch, adding new features, or taking over code written by others. We envision USEagent as the first draft of a future AI Software Engineer which can be a team member in future software development teams involving both AI and humans. To evaluate the efficacy of USEagent, we build a Unified Software Engineering bench (USEbench) comprising of myriad tasks such as coding, testing, and patching. USEbench is a judicious mixture of tasks from existing benchmarks such as SWE-bench, SWT-bench, and REPOCOD. In an evaluation on USEbench consisting of 1,271 repository-level software engineering tasks, USEagent shows improved efficacy compared to existing general agents such as OpenHands CodeActAgent. There exist gaps in the capabilities of USEagent for certain coding tasks, which provides hints on further developing the AI Software Engineer of the future.

Toon van 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.

Sajjad Hussain, R. Nawaz, Aysha Bibi

This research article investigates the effects of object's vibration and fluid movement on the acoustics of subsonic flows, specifically focusing on the scattering of acoustic waves by a vibrating rigid plate submerged in uniform flow. The acoustic plane wave that impacts the plate, coupled with its oscillatory motion, causes a disruption in the fluid medium. This disturbance, in turn, gives rise to a Rayleigh wave that propagates along the boundary separating the plate and the fluid. The study uses the Wiener‐Hopf technique to analytically model the acoustic scattering by a rigid barrier of finite dimensions and analyze the relationship between acoustics and structures. The method involves applying Fourier transformations to the governing boundary value problem and resolving the Wiener‐Hopf equations using the factorization theorem, Liouville's theorem, and analytical continuation. The integral equations of scattered potential computed asymptotically are used to describe the acoustic characteristics of structures and their interaction with fluid flow in subsonic conditions. The findings of the study reveal the sharp peaks of the scattered potential at certain angles with more oscillation in high subsonic flow. Also, increasing the frequency of the vibrating plate increases the amplitude of the scattered potential but is attenuated in mean flow whereas enhancing plate vibrations amplifies the scattered sound, and it is more vibrant in high subsonic flow than mean flow and no fluid flow. This research has applications in noise reduction, aeronautical engineering, and the detection of underwater structures using acoustic waves and micropolar elastic media.

Yan‐Feng Chen, Ze-Guo Chen, Hao Ge et al.

The last 20 years have witnessed growing impacts of the topological concept on the branches of physics, including materials, electronics, photonics, and acoustics. Topology describes objects with some global invariant property under continuous deformation, which in mathematics could date back to the 17th century and mature in the 20th century. In physics, it successfully underpinned the physics of the Quantum Hall effect in 1984. To date, topology has been extensively applied to describe topological phases in acoustic metamaterials. As artificial structures, acoustic metamaterials could be well theoretically analyzed, on‐demand designed, and easily fabricated by modern techniques, such as three‐dimensional printing. Some new theoretical topological models were first discovered in acoustic metamaterials analogous to electronic counterparts, associated with novel effects for acoustics closer to applications. In this review, we focused on the concept of topology and its realization in airborne acoustic crystals, solid elastic phononic crystals, and surface acoustic wave systems. We also introduced emerging concepts of non‐Hermitian, higher‐order, and Floquet topological insulators in acoustics. It has been shown that the topology theory has such a powerful generality that among the disciplines from electron to photon and phonon, from electronic to photonics and acoustics, from acoustic topological theory to acoustic devices, could interact and be analogous to fertilize fantastic new ideas and prototype devices, which might find applications in acoustic engineering and noise‐vibration control engineering in the near future.

Eita Shoji, Anis Maddi, Guillaume Penelet et al.

Phase-shifting interferometry is one of the optical measurement techniques that improves accuracy and resolution by incorporating a controlled phase shift into conventional optical interferometry. In this study, a four-step phase-shifting interferometer is developed to measure the spatio-temporal distribution of acoustic density oscillations of the gas next to a rigid plate. The experimental apparatus consists of a polarizing Mach-Zehnder interferometer with a polarization camera capable of capturing four polarization directions in one shot image and it is used to measure the magnitude and the phase of density fluctuations through a duct of rectangular cross-section connected to a loudspeaker. The results are compared with the well-established thermoacoustic theory describing the thermal coupling between acoustic oscillations and rigid boundaries, and the results show a very good agreement for various ratios of the (frequency-dependent) thermal boundary layer thickness to the plate spacing. This measurement technique could be advantageously employed to analyze more complex heat transfer processes involving the coupling of acoustic oscillations with rigid boundaries.

Nelly Bencomo, Jordi Cabot, Marsha Chechik et al.

Modern software-based systems operate under rapidly changing conditions and face ever-increasing uncertainty. In response, systems are increasingly adaptive and reliant on artificial-intelligence methods. In addition to the ubiquity of software with respect to users and application areas (e.g., transportation, smart grids, medicine, etc.), these high-impact software systems necessarily draw from many disciplines for foundational principles, domain expertise, and workflows. Recent progress with lowering the barrier to entry for coding has led to a broader community of developers, who are not necessarily software engineers. As such, the field of software engineering needs to adapt accordingly and offer new methods to systematically develop high-quality software systems by a broad range of experts and non-experts. This paper looks at these new challenges and proposes to address them through the lens of Abstraction. Abstraction is already used across many disciplines involved in software development -- from the time-honored classical deductive reasoning and formal modeling to the inductive reasoning employed by modern data science. The software engineering of the future requires Abstraction Engineering -- a systematic approach to abstraction across the inductive and deductive spaces. We discuss the foundations of Abstraction Engineering, identify key challenges, highlight the research questions that help address these challenges, and create a roadmap for future research.

Sichao Qu, Min Yang, Yunfei Xu et al.

In recent years, metamaterials have gained considerable attention as a promising material technology due to their unique properties and customizable design, distinguishing them from traditional materials. This article delves into the value of acoustic metamaterials in room acoustics, particularly in small room acoustics that poses specific challenges due to their significant cavity resonant nature. Small rooms usually exhibit an inhomogeneous frequency response spectrum, requiring higher wall absorption with specific spectrum to achieve a uniform acoustic environment, i.e., a constant reverberation time over a wide audible frequency band. To tackle this issue, we developed a design that simultaneously incorporates numerous subwavelength acoustic resonators at different frequencies to achieve customized broadband absorption for the walls of a specific example room. The on-site experimental measurements agree well with the numerical predictions, attesting to the robustness of the design and method. The proposed method of reverse-engineering metamaterials by targeting specific acoustic requirements has broad applicability and unique advantages in small confined spaces with high acoustic requirements, such as recording studios, listening rooms, and car cabins.

Cheol Jun Cho, Abdel-rahman Mohamed, Alan W. Black et al.

Self-Supervised Learning (SSL) based models of speech have shown remarkable performance on a range of downstream tasks. These state-of-the-art models have remained blackboxes, but many recent studies have begun “probing” models like HuBERT, to correlate their internal representations to different aspects of speech. In this paper, we show “inference of articulatory kinematics” as fundamental property of SSL models, i.e., the ability of these models to transform acoustics into the causal articulatory dynamics underlying the speech signal. We also show that this abstraction is largely overlapping across the language of the data used to train the model, with preference to the language with similar phonological system. Furthermore, we show that with simple affine transformations, Acoustic-to-Articulatory inversion (AAI) is transferrable across speakers, even across genders, languages, and dialects, showing the generalizability of this property. Together, these results shed new light on the internals of SSL models that are critical to their superior performance, and open up new avenues into language-agnostic universal models for speech engineering, that are interpretable and grounded in speech science.

Adam Kujawski, Art J. R. Pelling, S. Jekosch et al.

The use of machine learning for localization of sound sources from microphone array data has increased rapidly in recent years. Newly developed methods are of great value for hearing aids, speech technologies, smart home systems or engineering acoustics. The existence of openly available data is crucial for the comparability and development of new data-driven methods. However, the literature review reveals a lack of openly available datasets, especially for large microphone arrays. This contribution introduces a framework for generation of acoustic data for machine learning. It implements tools for the reproducible random sampling of virtual measurement scenarios. The framework allows computations on multiple machines, which significantly speeds up the process of data generation. Using the framework, an example of a development dataset for sound source characterization with a 64-channel array is given. A containerized environment running the simulation source code is openly available. The presented approach enables the user to calculate large datasets, to store only the features necessary for training, and to share the source code which is needed to reproduce datasets instead of sharing the data itself. This avoids the problem of distributing large datasets and enables reproducible research.

Carlo A. Furia, Richard Torkar, Robert Feldt

There is abundant observational data in the software engineering domain, whereas running large-scale controlled experiments is often practically impossible. Thus, most empirical studies can only report statistical correlations -- instead of potentially more insightful and robust causal relations. To support analyzing purely observational data for causal relations, and to assess any differences between purely predictive and causal models of the same data, this paper discusses some novel techniques based on structural causal models (such as directed acyclic graphs of causal Bayesian networks). Using these techniques, one can rigorously express, and partially validate, causal hypotheses; and then use the causal information to guide the construction of a statistical model that captures genuine causal relations -- such that correlation does imply causation. We apply these ideas to analyzing public data about programmer performance in Code Jam, a large world-wide coding contest organized by Google every year. Specifically, we look at the impact of different programming languages on a participant's performance in the contest. While the overall effect associated with programming languages is weak compared to other variables -- regardless of whether we consider correlational or causal links -- we found considerable differences between a purely associational and a causal analysis of the very same data. The takeaway message is that even an imperfect causal analysis of observational data can help answer the salient research questions more precisely and more robustly than with just purely predictive techniques -- where genuine causal effects may be confounded.

Juliano G. C. Ribeiro, Shoichi Koyama, Hiroshi Saruwatari

An interpolation method for region-to-region acoustic transfer functions (ATFs) based on kernel ridge regression with an adaptive kernel is proposed. Most current ATF interpolation methods do not incorporate the acoustic properties for which measurements are performed. Our proposed method is based on a separate adaptation of directional weighting functions to directed and residual reverberations, which are used for adapting kernel functions. Thus, the proposed method can not only impose constraints on fundamental acoustic properties, but can also adapt to the acoustic environment. Numerical experimental results indicated that our proposed method outperforms the current methods in terms of interpolation accuracy, especially at high frequencies.

Yuan Xie, Jiawei Ren, Ji Xu

Underwater acoustic target recognition is an intractable task due to the complex acoustic source characteristics and sound propagation patterns. Limited by insufficient data and narrow information perspective, recognition models based on deep learning seem far from satisfactory in practical underwater scenarios. Although underwater acoustic signals are severely influenced by distance, channel depth, or other factors, annotations of relevant information are often non-uniform, incomplete, and hard to use. In our work, we propose to implement Underwater Acoustic Recognition based on Templates made up of rich relevant information (hereinafter called "UART"). We design templates to integrate relevant information from different perspectives into descriptive natural language. UART adopts an audio-spectrogram-text tri-modal contrastive learning framework, which endows UART with the ability to guide the learning of acoustic representations by descriptive natural language. Our experiments reveal that UART has better recognition capability and generalization performance than traditional paradigms. Furthermore, the pre-trained UART model could provide superior prior knowledge for the recognition model in the scenario without any auxiliary annotation.

S. Saffar

* Department of Acoustics and Sound Engineering, IRIB University, P.O. Box 1986916511, Tehran, I.R. 4 Iran 5 saffar@iribu.ac.ir 6 Tel: +982177249832 7 Biographical notes: 8 I am cooperating with IRIBU as a Professor assistant in the Department of Acoustics and Audio 9 Engineering. I am teaching advance vibration, fundamental of acoustic and architectural 10 acoustic. I am also leading M.Sc. students who interest in application of acoustics in engineering 11 science. For instance, I am researching on the effect of acoustic waves on deactivating of hydatid 12 cyst in human without surgery, these days. I am also cooperating with Amirkabir University 13 (AUT) as a part time adjunct professor. I have gained valuable experiences leading M.Sc. and 14 B.Sc. students of both universities since 2013. 15 16 Abstract 17 High compressive strength concrete is desirable in the construction industry. The valuable effect 18 of high-power ultrasonic in the manufacturing industries is the motivation of this research in the 19 construction industry. For this purpose, high-power ultrasound was employed to increase the 20

Christopher Nies, M. Mellmann, B. Ankay et al.

The control and manipulation of acoustic and elastic waves is an important research topic in engineering sciences. In acoustics, an adequate combination of different materials can contribute to an efficient and broadband sound isolation. The realization of a vibration‐free environment for high‐precision mechanical systems in laboratories and measuring environments is also desirable in many practical cases. Therefore, advanced materials and structures with outstanding acoustic and elastodynamic properties are of great importance in engineering applications. In this paper, a numerical tool based on the finite element method (FEM) is developed for computing the dispersion relations or band structures of two‐dimensional (2D) phononic crystals (PCs) composed of an elastic matrix and periodically distributed cylindrical inclusions.

Xingda Li, Haozhe Zhang, Hongxing Tian et al.

In acoustic engineering, sound-proofing ventilation barriers find wide applications in diverse situations. However, most of the structures only have responses with fixed frequencies and a very narrow frequency range, especially for low frequency acoustics. Here we show a subwavelength acoustic metamaterial based on labyrinthine structures, which possesses tunable sound insulation and ventilation properties. The Fano-like asymmetric transmission dips is formed by the interference between the resonant scattering of discrete states and the background scattering of continuous states. By adjusting the spacing between these two half zigzag molds, the sound insulation dip frequency can shift from 360 Hz to 575 Hz while the free ventilation area ratio is kept to over 36.3% and the total thickness is only about 0.06λ. Moreover, the noise peak frequency can be detected by a microphone detection and adaptive adjustment of the spacing with a small stepping motor is demonstrated, the results agree well with numerical simulations. We anticipate our design may find potential applications in acoustic air vents, soundproofing window and duct noise control.

Zihao Su, Yifan Zhu, Siyuan Gao et al.

Acoustic insulation in ventilated structures is an important problem in acoustic engineering with many potential practical applications, such as the noise control for ventilating ducts of buildings, vehicles, or air conditioners. Acoustic metamaterial is a good candidate for the design of acoustic insulation for ventilated channel (AIVC) because the structural design with hard boundary has longer lifetime than conventional sound-absorbing cotton. In this paper, an AIVC with an open region and narrow channels of different lengths is proposed. We numerically and experimentally demonstrate its acoustic insulation larger than 20 dB (T < 0.01) within approximately 500–1,200 Hz with a subwavelength channel length of λ/6. The parameter dependence and air flow effect are numerically studied. Our findings show an alternative design of AIVC that may have applications in noise control and architectural acoustics.