Hasil untuk "Acoustics in engineering. Acoustical engineering"

Berdau Martin, Alcala Padilla Daniel-José, Brand Thomas et al.

This study investigated real-time assessment and modeling of perceived listening effort (LE). The model consists of a binaural front-end, followed by a monaural back-end. As front-end, a novel blind real-time implementation of the binaural speech intelligibility model (BSIM) was developed, which models spatial release from masking by considering binaural unmasking and better-ear listening simultaneously. A neural network was used as back-end, which was trained on inputs and outputs of a LE prediction model based on phoneme classification called Listening Effort prediction from Acoustic Parameters (LEAP). A novel method for evaluating binaural real-time models of LE was developed, where simulated scenes with a target speaker and a noise interferer were used, which were either co-located or spatially separated. Dynamic changes were introduced to the scene by abruptly altering the signal-to-noise ratio and/or reverberation time. Participants continuously rated subjectively perceived LE using a slider interface with LE categories, while listening to the scenes via headphones. The model accurately predicted subjective LE, especially changes in signal-to-noise ratio and binaural benefits. It also predicted detrimental effects of reverberation as observed in the experiment, although the impact of reverberation was slightly overestimated. Human response times were estimated for further tweaking the model’s integration time.

Al-Hajj Abdulatef, Zhou Jiacheng, Zhang Bo et al.

Phased-array acoustic tweezers serve as powerful tools for non-contact microparticle manipulation; however, achieving stable and repeatable three-dimensional control remains a significant challenge in physical acoustics. Here, we introduce an open-source, modular Bilateral Array ultrasonic platform designed to generate both twin and vortex acoustic fields for precise particle actuation. By integrating Arduino-based logic with a custom 16-channel MOSFET driver board, the system enables real-time phase management of 128 transducers at 40 kHz. We demonstrate that this bilateral array configuration significantly enhances acoustic focusing, manipulation accuracy, and field stability compared to traditional unilateral setups. Through rigorous experimentation, we quantify the distinct stability profiles of different field topologies generated by this system. Results indicate that while vortex fields effectively induce rotation (σθ = 2.5°), they suffer from inherent vertical instability (σz ≈ 0.10 cm), resulting in particle ejection at higher elevations. Conversely, the twin configuration demonstrates superior confinement, achieving sub-millimeter precision in both horizontal (σx ≤ 0.03 cm) and vertical (σz ≤ 0.04 cm) planes. This stability facilitates complex multi-particle operations, including the synchronized rotation of four particles and controlled merging. These findings establish quantitative design guidelines for acoustic field selection, delineating the operational trade-offs between rotational torque and axial confinement for microfluidic and biomedical applications.

Alexandra González, Xavier Franch, Silverio Martínez-Fernández

Integrating Artificial Intelligence into Software Engineering (SE) requires having a curated collection of models suited to SE tasks. With millions of models hosted on Hugging Face (HF) and new ones continuously being created, it is infeasible to identify SE models without a dedicated catalogue. To address this gap, we present SEMODS: an SE-focused dataset of 3,427 models extracted from HF, combining automated collection with rigorous validation through manual annotation and large language model assistance. Our dataset links models to SE tasks and activities from the software development lifecycle, offering a standardized representation of their evaluation results, and supporting multiple applications such as data analysis, model discovery, benchmarking, and model adaptation.

Mairieli Wessel, Daniel Feitosa, Sangeeth Kochanthara

Rising publication pressure and the routine use of generative AI tools are reshaping how software engineering research is produced, assessed, and taught. While these developments promise efficiency, they also raise concerns about skill degradation, responsibility, and trust in scholarly outputs. This vision paper employs Design Fiction as a methodological lens to examine how such concerns might materialise if current practices persist. Drawing on themes reported in a recent community survey, we construct a speculative artifact situated in a near future research setting. The fiction is used as an analytical device rather than a forecast, enabling reflection on how automated assistance might impede domain knowledge competence, verification, and mentoring practices. By presenting an intentionally unsettling scenario, the paper invites discussion on how the software engineering research community in the future will define proficiency, allocate responsibility, and support learning.

Golnoush Abaei, Mojtaba Shahin, Maria Spichkova

The inclusion of internship courses in Software Engineering (SE) programs is essential for closing knowledge gaps and improving graduates' readiness for the software industry. Our study focuses on year-long internships at RMIT University (Melbourne, Australia), which offers in-depth industry engagement. We analysed how the course evolved over the last 10 years to incorporate students' needs and summarised the lessons learned that can be helpful for other educators supporting internship courses. Our qualitative analysis of internship data based on 91 reports during 2023-2024 identified three challenge themes the students faced, and which courses were found by students to be particularly beneficial during their internships. On this basis, we proposed recommendations for educators and companies to help interns overcome challenges and maximise their learning experience.

Massimiliano Di Penta, Kelly Blincoe, Marsha Chechik et al.

As software engineering conferences grow in size, rising costs and outdated formats are creating barriers to participation for many researchers. These barriers threaten the inclusivity and global diversity that have contributed to the success of the SE community. Based on survey data, we identify concrete actions the ACM Special Interest Group on Software Engineering (SIGSOFT) can take to address these challenges, including improving transparency around conference funding, experimenting with hybrid poster presentations, and expanding outreach to underrepresented regions. By implementing these changes, SIGSOFT can help ensure the software engineering community remains accessible and welcoming.

Ana B. M. Bett, Thais S. Nepomuceno, Edson OliveiraJr et al.

Context: The empirical software engineering (ESE) community has contributed to improving experimentation over the years. However, there is still a lack of rigor in describing controlled experiments, hindering reproducibility and transparency. Registered Reports (RR) have been discussed in the ESE community to address these issues. A RR registers a study's hypotheses, methods, and/or analyses before execution, involving peer review and potential acceptance before data collection. This helps mitigate problematic practices such as p-hacking, publication bias, and inappropriate post hoc analysis. Objective: This paper presents initial results toward establishing an RR template for Software Engineering controlled experiments using the Open Science Framework (OSF). Method: We analyzed templates of selected OSF RR types in light of documentation guidelines for controlled experiments. Results: The observed lack of rigor motivated our investigation of OSF-based RR types. Our analysis showed that, although one of the RR types aligned with many of the documentation suggestions contained in the guidelines, none of them covered the guidelines comprehensively. The study also highlights limitations in OSF RR template customization. Conclusion: Despite progress in ESE, planning and documenting experiments still lack rigor, compromising reproducibility. Adopting OSF-based RRs is proposed. However, no currently available RR type fully satisfies the guidelines. Establishing RR-specific guidelines for SE is deemed essential.

von Berg Markus, Versümer Siegbert, Bitta Joshua et al.

Laboratory experiments in psychoacoustical and soundscape research indicate that participants perceive a reproduction sound level lowered by 8–10 dB as more plausible than the original level. This bias supposedly roots in an adaptation of perceptual loudness scaling to the laboratory environment, that is overall quieter than urban outdoor soundscapes. To gain further insights into the nature of such loudness bias, we conducted a listening experiment in both field and laboratory using a within-subjects design. Thirty-one participants visited a street and listened to the environmental sounds for one minute, while these sounds were also recorded using a dummy head. Thereafter, they listened to the recording in a quiet laboratory nearby and adjusted its level as they remembered it. About half of the sample did this immediately, the other half about 20 min after the recording. Results confirm a bias towards lower levels with a mean of about 8.9 dB, regardless of the time between the recording and the reproduction in the laboratory. Also, participants with higher musical abilities tended to select higher, more accurate levels, whereas noise-sensitive participants deemed lower levels appropriate. Results suggest that the hypothesized adaptation of perceptual scaling to the laboratory happens immediately and is affected by individual factors.

Zirui Li, Stephan Husung, Haoze Wang

Cross-organizational collaboration in Model-Based Systems Engineering (MBSE) faces many challenges in achieving semantic alignment across independently developed system models. SysML v2 introduces enhanced structural modularity and formal semantics, offering a stronger foundation for interoperable modeling. Meanwhile, GPT-based Large Language Models (LLMs) provide new capabilities for assisting model understanding and integration. This paper proposes a structured, prompt-driven approach for LLM-assisted semantic alignment of SysML v2 models. The core contribution lies in the iterative development of an alignment approach and interaction prompts, incorporating model extraction, semantic matching, and verification. The approach leverages SysML v2 constructs such as alias, import, and metadata extensions to support traceable, soft alignment integration. It is demonstrated with a GPT-based LLM through an example of a measurement system. Benefits and limitations are discussed.

Jake Zappin, Trevor Stalnaker, Oscar Chaparro et al.

This position paper examines the substantial divide between academia and industry within quantum software engineering. For example, while academic research related to debugging and testing predominantly focuses on a limited subset of primarily quantum-specific issues, industry practitioners face a broader range of practical concerns, including software integration, compatibility, and real-world implementation hurdles. This disconnect mainly arises due to academia's limited access to industry practices and the often confidential, competitive nature of quantum development in commercial settings. As a result, academic advancements often fail to translate into actionable tools and methodologies that meet industry needs. By analyzing discussions within quantum developer forums, we identify key gaps in focus and resource availability that hinder progress on both sides. We propose collaborative efforts aimed at developing practical tools, methodologies, and best practices to bridge this divide, enabling academia to address the application-driven needs of industry and fostering a more aligned, sustainable ecosystem for quantum software development.

Martin Obaidi, Marc Herrmann, Jil Klünder et al.

Software development relies heavily on text-based communication, making sentiment analysis a valuable tool for understanding team dynamics and supporting trustworthy AI-driven analytics in requirements engineering. However, existing sentiment analysis tools often perform inconsistently across datasets from different platforms, due to variations in communication style and content. In this study, we analyze linguistic and statistical features of 10 developer communication datasets from five platforms and evaluate the performance of 14 sentiment analysis tools. Based on these results, we propose a mapping approach and questionnaire that recommends suitable sentiment analysis tools for new datasets, using their characteristic features as input. Our results show that dataset characteristics can be leveraged to improve tool selection, as platforms differ substantially in both linguistic and statistical properties. While transformer-based models such as SetFit and RoBERTa consistently achieve strong results, tool effectiveness remains context-dependent. Our approach supports researchers and practitioners in selecting trustworthy tools for sentiment analysis in software engineering, while highlighting the need for ongoing evaluation as communication contexts evolve.

Dehai Zhao, Zhenchang Xing, Qinghua Lu et al.

UI automation is a useful technique for UI testing, bug reproduction, and robotic process automation. Recording user actions with an application assists rapid development of UI automation scripts, but existing recording techniques are intrusive, rely on OS or GUI framework accessibility support, or assume specific app implementations. Reverse engineering user actions from screencasts is non-intrusive, but a key reverse-engineering step is currently missing - recognizing human-understandable structured user actions ([command] [widget] [location]) from action screencasts. To fill the gap, we propose a deep learning-based computer vision model that can recognize 11 commands and 11 widgets, and generate location phrases from action screencasts, through joint learning and multi-task learning. We label a large dataset with 7260 video-action pairs, which record user interactions with Word, Zoom, Firefox, Photoshop, and Windows 10 Settings. Through extensive experiments, we confirm the effectiveness and generality of our model, and demonstrate the usefulness of a screencast-to-action-script tool built upon our model for bug reproduction.

Marmel Frédéric, Cretagne Léo, Thuong Linh-Thao et al.

This study aimed to quantify, in situations representative of the daily life of European citizens, the effects of sonic boom exposure on human responses, in the case of a new generation of supersonic commercial aircraft that should emit a reduced (compared to the past generation like Concorde) but perceivable boom while flying overland. Two reduced boom simulators were affixed to the bedrooms’ windows of a house located on our university campus. The simulators were used to study indoor the participants’ responses to realistic “outdoor” booms. Testing took place in both the living room and kitchen because the booms caused different intensities of rattle noise in those two rooms. Participants performed various tasks (communication, working memory, drawing, valence evaluation), took three mandatory rests and filled in various questionnaires about the annoyance caused by the booms and their mood. This paper focuses on the psychophysical and cognitive performance results. The booms resulted in delayed responses in the working memory task and in the valence evaluation task, and in a momentary slowing down in the drawing task. There was no significant effect in the communication task, even though a trend for a worsening of communication efficiency was observed. Taken together, the results suggest that reduced booms can interfere with cognitive and motor tasks by capturing attention, which can momentarily divert cognitive resources away from the task at hand. These results suggest future research directions and may lead to recommendations for future sonic boom regulations.

Cillo Pierfrancesco, Brauchler Alexander, Gonzalez Sebastian et al.

Recently developed high-fidelity finite element (FE) models represent a state-of-the-art approach for gaining a deeper understanding of the vibrational behavior of musical instruments. They can also be used as virtual prototypes. However, certain analyses, such as optimization or parameter identification, necessitate numerous model evaluations, resulting in long computation times when utilizing the FE model. Projection-based parametric model order reduction (PMOR) proves to be a powerful tool for enhancing the computational efficiency of FE models while retaining parameter dependencies. Despite their advantages, projection-based methods often require complete system matrices, which may have limited accessibility. Consequently, a systematic discrepancy is introduced in the reduced-order model compared to the original model. This contribution introduces a discrepancy modeling method designed to approximate the parameter-dependent effect of a radiating boundary condition in an FE model of a classical guitar that cannot be exported from the commercial FE software Abaqus. To achieve this, a projection-based reduced-order model is augmented by a data-driven model that captures the error in the approximation of eigenfrequencies and eigenmodes. Artificial neural networks account for the data-driven discrepancy models. This methodology offers significant computational savings and improved accuracy, making it highly suitable for far-reaching parametric studies and iterative processes. The combination of PMOR and neural networks demonstrate greater accuracy than using either approach alone. This paper extends our prior research presented in the proceedings of Forum Acusticum 2023, offering a more comprehensive examination and additional insights.

Prislan Rok, Kržič Urša, Svenšek Daniel

Timbre – sound “colour” – is an abstract, delicate property of sound, especially in a high-value context such as musical instruments. It is a perceptual construct so intangible that it cannot be considered a quantity. Since sound nevertheless reaches our ears as a complete physical reality, we hypothesize that this inherent abstraction of its timbre is primarily due to the lack of a meaningful, musically relevant, and robust quantification that would do justice to the subtlety of human auditory perception. It is therefore not surprising that not a single aspect of timbre is to be found in the specifications of musical instruments. We introduce harmonic timbre coordinates, concrete and robust numbers that quantify a partial aspect of timbre of an instrument’s sound – its harmonic structure – with a precision that allows relevance in the musical context. These numbers could, for example, help a buyer find an instrument whose sound is closer to his or her preferences. Or they could enable precise tracking of harmonic changes in sound, and more.

Jialiang Wei, Anne-Lise Courbis, Thomas Lambolais et al.

Graphical User Interfaces (GUIs) are central to app development projects. App developers may use the GUIs of other apps as a means of requirements refinement and rapid prototyping or as a source of inspiration for designing and improving their own apps. Recent research has thus suggested retrieving relevant GUI designs that match a certain text query from screenshot datasets acquired through crowdsourced or automated exploration of GUIs. However, such text-to-GUI retrieval approaches only leverage the textual information of the GUI elements, neglecting visual information such as icons or background images. In addition, retrieved screenshots are not steered by app developers and lack app features that require particular input data. To overcome these limitations, this paper proposes GUing, a GUI search engine based on a vision-language model called GUIClip, which we trained specifically for the problem of designing app GUIs. For this, we first collected from Google Play app introduction images which display the most representative screenshots and are often captioned (i.e.~labelled) by app vendors. Then, we developed an automated pipeline to classify, crop, and extract the captions from these images. This resulted in a large dataset which we share with this paper: including 303k app screenshots, out of which 135k have captions. We used this dataset to train a novel vision-language model, which is, to the best of our knowledge, the first of its kind for GUI retrieval. We evaluated our approach on various datasets from related work and in a manual experiment. The results demonstrate that our model outperforms previous approaches in text-to-GUI retrieval achieving a Recall@10 of up to 0.69 and a HIT@10 of 0.91. We also explored the performance of GUIClip for other GUI tasks including GUI classification and sketch-to-GUI retrieval with encouraging results.

Blumendeller Esther, Gaßner Laura, Müller Florian J.Y. et al.

Amplitude modulation (AM) is a common phenomenon associated with wind turbine (WT) related noise annoyance. Within the interdisciplinary project Inter-Wind, acoustic, ground motion, and meteorological data are captured to be evaluated with noise reports of residents living near a wind farm in Southern Germany. The recorded data builds a solid data base for the evaluation of AM. The occurrence of AM is detected within acoustic and ground motion data and set in relation to all available data, including WT operational parameters, meteorology, and noise reports. In this study, the origins of detected AM are tones at 57.8 Hz and 133 Hz, related to the generator and drive train, which are amplitude modulated by the blade passing frequency. AM detection was successful both with acoustic as well as ground motion data. A comparison of a method for AM detection developed by the Institute of Acoustics (IOA reference method) with a method specifically developed to detect AM in ground motion data showed that the reference method detected AM three to six times more often than the newly developed method. AM occurred most likely during stable atmospheric conditions, with a positive lapse rate, and was (albeit to a small degree) more likely to be detected when residents reported higher levels of annoyance.

Lluís Francesc, Meyer-Kahlen Nils, Chatziioannou Vasileios et al.

Ambisonics is a scene-based spatial audio format that has several useful features compared to object-based formats, such as efficient whole scene rotation and versatility. However, it does not provide direct access to the individual source signals, so that these have to be separated from the mixture when required. Typically, this is done with linear spherical harmonics (SH) beamforming. In this paper, we explore deep-learning-based source separation on static Ambisonics mixtures. In contrast to most source separation approaches, which separate a fixed number of sources of specific sound types, we focus on separating arbitrary sound from specific directions. Specifically, we propose three operating modes that combine a source separation neural network with SH beamforming: refinement, implicit, and mixed mode. We show that a neural network can implicitly associate conditioning directions with the spatial information contained in the Ambisonics scene to extract specific sources. We evaluate the performance of the three proposed approaches and compare them to SH beamforming on musical mixtures generated with the musdb18 dataset, as well as with mixtures generated with the FUSS dataset for universal source separation, under both anechoic and room conditions. Results show that the proposed approaches offer improved separation performance and spatial selectivity compared to conventional SH beamforming.

Rust Marvin, Kling Christoph, Koch Christian et al.

With the growing prevalence of infrasound and potential for annoyance comes the need for noise assessment. Performance validation of measuring instruments is an established necessity for reliable measurement data at conventional frequencies. However, infrasound measurements are critically dependent on the integrity of the microphone. A case study is presented showing that errors in excess of 20 dB result if the microphone diaphragm is perforated, and that such a defect cannot be detected by visual examination or with a typical sound calibrator. A further laboratory study validates the findings, and a scheme is proposed for identifying when such an issue exists.

Jeong Jeongho, Ryu Jongkwan

The rubber ball impact sound has been standardized by ISO 10140 series and ISO 16283-2 for laboratory and field measurements, respectively. The ISO 10052 standard specifies a survey method for the impact sound measurement using a tapping machine and a rubber ball. This study proposed measurement position for the survey method which is highly correlated with result based on the Korean Standards (KS) and the building regulation of South Korea for engineering method. The rubber ball impact sounds were measured in 79 reinforced concrete apartment houses, which have a centre point and four perimeter points for both exciting and receiving sounds. The proposed survey method was validated for only a specific type of apartment building layout and construction in the South Korean environment. The excitation and receiving points in the perimeter having the most similar characteristics to the results obtained using the Korean engineering methods were first selected. By combining the selected perimeter point and centre point for both the excitation and receiving sounds, the characteristics of each combination were compared with the results obtained using the Korean engineering method. When one excitation point or receiving point in the perimeter was added to the centre point for the proposed survey method, the difference between the measurement result using the engineering and proposed survey method decreased. The standard deviation of the difference between the SNQs of the proposed survey method and the Korean engineering method for measuring the rubber ball impact sound was smaller than 2 dB.