Search Results (443)

Poor classroom acoustic conditions can impair speech intelligibility, increase cognitive load, and reduce the quality of learning environments. Simulation-guided optimization provides a promising approach for improving building acoustic performance and indoor environmental quality while reducing trial-and-error material selection in renovation practice. The field-validated optimized configuration combined slotted wood sound-absorbing panels and mineral wool panels, with a total material cost of 1512 RMB. Field measurements showed that this configuration reduced RT from 2.42–1.76 s to 0.78–0.43 s. In addition, the simulation-based STI evaluation increased from 0.19 to 0.75, indicating a potential improvement in speech intelligibility. Since STI was not directly measured in the field, this result is interpreted as a model-based prediction supported by the calibrated RT validation. The average relative error between simulated and measured RT values was 6.83%, demonstrating the predictive reliability of the calibrated model for RT prediction. The proposed framework provides a practical decision-support method for cost-controlled acoustic renovation of educational buildings and was validated using an existing classroom case. The framework was demonstrated using one existing classroom and provides a methodological basis for adaptation to other educational spaces by updating room-specific inputs. Its external transferability requires validation in additional classrooms. Full article

The sound reduction index R is commonly determined using standardized laboratory procedures developed primarily for full-size building elements. However, in many research and development applications, including technical enclosures, lightweight panels, modular components, and new acoustic materials, only reduced-size specimens are available. In such cases, the influence of specimen dimensions and test-opening geometry on the measured sound insulation is not yet fully understood. This study investigates the effect of specimen size and geometry on the measured sound reduction index using a dedicated small-scale coupled reverberation room stand. Measurements were performed for five materials with different mechanical and structural properties: steel, polymethyl methacrylate (PMMA), medium-density fiberboard (MDF), gypsum board, and Sylomer. Six test openings were analyzed, including three square openings, one quasi-square opening, and two rectangular openings. The results show that specimen dimensions can significantly affect the measured values of R, especially in the low-frequency range, where modal behavior, boundary conditions, and the relationship between specimen dimensions and acoustic wavelength are important. The influence of specimen size was material-dependent and was more pronounced for stiff plate-like materials than for the highly compliant Sylomer specimen. Comparisons between square and rectangular openings with similar surface areas suggest that, within the investigated range of materials, specimen geometries, and measurement conditions, specimen surface area had a greater influence on R than specimen shape, although geometry can still contribute to the measured differences. The repeatability analysis confirmed that the measurement stand is sensitive to differences related to material type, specimen dimensions, and installation conditions. The proposed methodology may be particularly useful for comparative studies of novel acoustic materials and prototype building elements when only reduced-size specimens are available during the early stages of material development. The results support the use of small coupled reverberation rooms for comparative testing and preliminary material screening, while also showing that reduced-size sound insulation measurements require careful interpretation and cannot be treated as direct substitutes for full-scale standardized tests. Full article

This paper presents a novel two-step algorithm for microphone array fusion to enhance sound source localization (SSL) in three-dimensional indoor reverberant environments. Simulation analyses using simulated Room Impulse Responses (RIRs) reveal that Angle-of-Arrival (AoA) accuracy varies significantly with source position, causing certain microphone arrays to produce unreliable estimates. To mitigate this, the algorithm excludes microphone pairs with low-confidence AoAs, thereby improving overall localization accuracy. To extend the applicability of the approach, a generalized version of the algorithm is proposed for arbitrary room geometries and array positions on each wall. Its performance is assessed across three scenarios: (1) the original room geometry with arrays placed at the center of each wall; (2) a room with different dimensions; and (3) arrays placed at arbitrary positions on walls. The results show that the generalized algorithm achieves similar improvements as the original two-step method, approximately halving the localization error. Moreover, while room geometry and array placement influence SSL accuracy, the generalized method consistently reduces error across all cases. Three conventional AoA estimation methods are evaluated and their performance is compared in the baseline SSL. These findings highlight the robustness and practical value of the proposed algorithm on the baseline methods for improving SSL performance in acoustically challenging environments. Full article

Acoustic survey provides a measurement-based approach for investigating heritage spaces in which architectural morphology, environmental conditions, and sound-related practices are physically interrelated. This study applies a portable and non-invasive survey protocol to the medieval cave sanctuary of San Michele di Mezzo, located in Fisciano, Southern Italy. The site consists of stratified natural and built spaces, including a lower cave, an upper cave, and a later upper church, and represents a relevant case study for assessing the acoustic behaviour of small, irregular, and fragile cultural heritage environments. The experimental procedure combined calibrated microphone recordings, time-domain signal inspection, third-octave-band analysis, and impulse-response-derived room-acoustic indicators, including reverberation, clarity, and definition parameters. Under the adopted source–receiver configurations, the results show acoustic differentiation among the lower cave, upper cave, and later church. The caves exhibit shorter decay times than the church over most frequency bands, while clarity and definition indicators reveal a frequency-dependent behaviour that does not support a general claim of the acoustic superiority of one space over another. Comparative data from other cave and cave-like environments further contextualize the measured response of San Michele di Mezzo. The findings do not imply intentional acoustic design; rather, in the measured configuration, they show that, under the chosen conditions, the long-lasting devotional centrality of the lower cave is compatible with an acoustic response that does not contradict spoken or sung devotional use. More broadly, the study contributes to applied acoustics by demonstrating that low-invasive field surveys can provide reproducible acoustic indicators for heritage interpretation, conservation-oriented documentation, and the investigation of intangible sound-related dimensions of cultural heritage. Full article

Reverberation and background noise remain persistent obstacles to achieving clear and intelligible speech in enclosed environments. Conventional data-driven or purely empirical dereverberation systems often perform well only under training conditions but lack robustness and physical interpretability when exposed to new acoustic spaces. To address these limitations, this paper proposes a physics-informed speech enhancement algorithm that integrates analytical room acoustics modeling with a descriptor-guided optimization framework. The method employs virtual field simulations based on the Helmholtz equation to estimate key acoustic descriptors, reverberation time (RT60), direct-to-reverberant ratio (DRR), and clarity index (C50), which are then used to adaptively control a model-informed dereverberation filter. This hybrid formulation bridges physical modeling and signal processing, allowing the algorithm to minimize late reverberation energy while maintaining spectral fidelity. Experimental results across multiple simulated and real-room conditions demonstrate measurable improvements over baseline methods, achieving average gains of +6.4 dB in SNR, +1.2 in PESQ, and +0.13 in STOI, along with reduced RT60 and enhanced clarity. The proposed approach offers both computational efficiency and interpretability, making it suitable for real-time deployment in teleconferencing, hearing-assistive, and smart audio applications. Full article

Respiratory disease remains a major challenge in pig production. This two-room pilot study evaluated whether room-level acoustic monitoring combined with physiological measurements could provide an early warning after an experimental Klebsiella pneumoniae challenge. Forty growing pigs balanced by sex and body weight were housed for 28 days in one control room and one challenged room (20 pigs/room; four pens/room). Challenged pigs were intranasally inoculated on days 8, 12, 16, and 20 with a culture whose dose was retrospectively verified by serial-dilution plating. Nasal and fecal samples were cultured on Klebsiella ChromoSelect agar, and colonies with expected morphology were enumerated as presumptive Klebsiella/K. pneumoniae colonies. A fine-tuned Audio Spectrogram Transformer (AST) classified five sound classes from facility-specific audio and was evaluated by group-blocked hold-out testing, five-fold group-blocked cross-validation, temporal deployment validation, and window-threshold sensitivity analysis. The model achieved hold-out macro-F1 of 0.947, five-fold macro-F1 of 0.928 ± 0.019, and 24 h deployment macro-F1 of 0.914. Presumptive nasal bacterial load was higher in challenged pigs at 1-week post-inoculation (log₁₀ 4.03 vs. 0.67). Group-size-standardized cough detections were also higher in the challenged room (54.84 vs. 36.80 detections/day), and daily coughing first exceeded the baseline threshold on day 8. Thresholds of 0.764 (control) and 1.115 (treatment) were obtained from an integrated score that included coughing, sneezing, ear temperatures, rectal temperature, and respiration rate; the treatment score and treatment–control contrast score first surpassed the threshold on day 8, and daily multimodal scores varied between groups (t = −6.636, p < 0.001). Integrated score improved discrimination of post-inoculation disturbance compared with cough detections alone (leave-one-day-out AUROC: 0.94 vs. 0.88). Because each condition was represented by one room, findings are exploratory temporal contrasts, not replicated treatment effects or a stand-alone diagnostic test. Full article

This study examines the potential of sustainable, biobased paper-based structures as panel/membrane sound absorbers. Although intact paper is naturally impermeable and a poor sound absorber, transforming it into complex three-dimensional origami geometries, specifically the Miura-ori pattern, could produce effective panel/membrane absorbers. Three distinct Miura-ori samples (A, B, and C) were fabricated with increasing geometric complexity, ranging from a simple triangular prism to a complex labyrinthine waveguide. The random incidence sound absorption coefficients of these samples were measured in a validated small-scale reverberation room. The underlying absorption mechanisms were further investigated through modal analysis and non-contact vibration velocity measurements. The results indicate that increased geometric complexity enhances acoustic performance. Sample C, the most complex structure, demonstrated the most consistent broadband absorption. The analysis confirmed a significant positive correlation between acoustic pressure modes, surface vibration velocity, and sound absorption peaks, indicating that acoustic energy dissipation is driven by the vibrational response of the paper membrane coupled with resonant modes in the air gap. This research demonstrates that tunable origami folding techniques using intact paper can be used to design lightweight acoustic treatments for diffuse sound fields in the mid-frequency range. Full article

To realize the real-time structural health and operational safety monitoring of military and industrial devices, such as hypersonic vehicles, aero-engine blades, and thermal power plant boilers, at operating temperatures up to and beyond 1400 °C, this study presents a miniaturised, integrated, high-thermal-stability wireless sensor device. This study investigated the influence of temperature on the interdigital electrodes (IDEs) of surface acoustic wave (SAW) temperature sensors for three configurations: bare electrode, single-layer protective film, and multilayer composite film. While the exposed electrode exhibited thermal stability at 1000 °C, it underwent structural failure at 1250 °C. To achieve health monitoring at temperatures exceeding 1400 °C, an Al₂O₃/AlN/Al₂O₃ multilayer protective architecture was developed. The device demonstrated functionality up to 1400 °C with a temperature coefficient of frequency (TCF) of −40.03 ppm/°C, yielding a sensitivity of 12.0 kHz/°C at a center frequency of ~300 MHz. The electrode protection structure elevated the maximum operating temperature. A hybrid heterogeneous integration of high-temperature co-fired ceramic (HTCC) inverted-F antenna and a Langasite (LGS) SAW device with a multilayer composite film was realised. The wireless device maintained functionality from room temperature to 1400 °C and withstood 1400 °C for 2 h, exhibiting a maximum repeatability error of 12.67% (corresponding to a temperature measurement error of ~177.4 °C at 1400 °C). This integrated design enables the miniaturization of high-temperature wireless sensors, making them suitable for harsh environments. Full article

Manual counting of surgical tools, known as surgical counting, is a time-consuming and error-prone task that increases the risk of retained surgical instruments and extends operating room (OR) time. Presently, in hospitals around the world, surgical counting is often performed manually with paper or tablet checklists, often leading to delays, increased infection risk, and financial cost. RFID, barcode-based, and computer vision solutions exist but are expensive and have challenges with sterilization and signal interference. This paper presents ToolSafe, a low-cost, portable system that classifies surgical tools by their acoustic signatures when dropped into a detection box. A pilot dataset of 4004 audio samples from four tool types (n = 996, tissue forceps; n = 1005, iris scissors; n = 1006, scalpel handle; n = 997, testing needle) was collected using ToolSafe. A convolutional neural network (CNN) was evaluated using stratified five-fold cross-validation on the laboratory dataset, with a k-nearest neighbors (KNN) classifier implemented as a control model. In each fold, both models were trained on 80% of the data and tested on the remaining 20%, ensuring that all samples were used for both training and evaluation. The CNN achieved a mean (±standard deviation) classification accuracy of 99.55% (±0.19%) across the validation folds, outperforming the KNN model, which achieved a mean accuracy of 97.28% (±0.50%). The difference was statistically significant according to a paired t-test across folds (p = 0.0003), indicating CNN’s superior performance on the dataset. For a run of 100 additional samples using the Raspberry Pi-based system, spectrogram generation averaged 0.121 s (±0.025 s), CNN inference averaged 0.180 s (±0.033 s), and total end-to-end latency averaged 1.851 s (±0.253 s) per tool. This pilot study proposes a possible technological solution for surgical counting that reduces human error and enhances patient safety. ToolSafe may be subsequently improved by increasing the number of surgical tools used in the training dataset, testing under more robust OR-like environments, and comparing to other classification algorithms. Further refinement and incorporation of ToolSafe in operating room workflows have the potential to reduce patient risks from extended surgical times and retained surgical instruments. Full article

To ensure the reliable operation of speech systems across diverse environments, noise addition methods have emerged as the standard solution. However, existing methods offer limited coverage of real-world scenes and depend on pre-existing noise libraries and scene metadata. This paper presents prompt-based Dynamic Generative Scene-based Noise Addition (DGSNA), a novel approach driven by generative language models that integrates Dynamic Generation of Scene-based Information (DGSI) with Scene-based Noise Addition for Speech (SNAS). The DGSI module, with a BET (Background, Examples, Task) prompt framework, dynamically generates logic-compliant scene-based information, including scene dimensions, sound sources, and microphone positions, thereby addressing the challenges of scene enumeration and detailed description. Complementing this, the SNAS module employs a Time–Frequency Diffusion-based (TFD) Text-to-Audio model to synthesize scene-specific noise. By integrating this noise with clean speech via Room Impulse Response (RIR) filters, the module streamlines the traditionally labor-intensive process of replicating diverse acoustic environments. Experimental results show that DGSNA significantly enhances the robustness of speech recognition and keyword spotting models, achieving relative improvements of up to 11.32%. Furthermore, DGSNA is highly compatible with existing noise addition techniques. Full article

Comprehensive room acoustic characterization requires resolving reflection behavior across time, frequency, and space. The recently proposed eigenbeam–vMF-based analyzer provides a framework for this by modeling the reflection field as a time–frequency-dependent directional power distribution, estimated via spatial correlation of eigenbeams (ambisonics) and parameterized using von Mises–Fisher clustering. This formulation enables a unified and interpretable description of anisotropic early reflections, their transition into diffuse reverberation, and frequency-dependent acoustic behavior. Prior work showed that the analyzer reliably captures these features using higher-order ambisonics from a 32-channel spherical microphone array (SMA) and that constraining the same array to the first order still led to retaining the dominant features. This paper investigates whether this capability extends to first-order microphone arrays with sparser spatial sampling for more economical and practical deployment. A comparative study is conducted in a recording studio with variable wall panels (wood and felt), evaluating a four-channel first-order array against a 32-channel SMA. The results reveal distinct acoustic differences between panel settings, which are consistent across both arrays. While the SMA captures finer spatial detail and prolonged anisotropic reflections more effectively, the first-order array demonstrates potential for preliminary room acoustic assessments by identifying room mode frequencies, dominant reflection directions, and highly reflective surfaces. Full article

The Real Maestranza de Caballería in Seville features one of the most prominent Spanish bullrings, characterized by a notable architectural design. Its distinctive ovoid geometry resulted from a protracted construction history (1761–1881), during which the floor plan adapted to pre-existing urban structures. Beyond its architectural significance, the sounds perceived within such venues constitute traces of collective memory and form part of an intangible cultural heritage relevant for understanding the sociocultural context of such spaces. This work provides an acoustic characterisation of the bullring through field measurements. Reverberation time and other monaural and binaural descriptors were determined using 3D impulse responses obtained from strategically placed sources and receivers. This analysis is complemented by examining the sound energy distribution of early reflections in the time–frequency domain to define the acoustic signature of the venue, namely the characteristic pattern of early reflections that unequivocally determines its sound response, and identify the provenance of reflections. In the Maestranza, music and silence are hallmarks of its identity, contributing to a complex auditory environment. The results highlight how its geometry and tiered seating create a differentiated sound field, potentially contributing to the preservation of the site as a cultural landmark. Full article

Room acoustics computer models based on geometrical acoustics usually handle the sound reflections by the assumption of plane waves. However, if the sound source is a point source, which is usually the case, the spherical wave reflection would be more correct. An approximate model for the spherical wave reflection is presented, starting with the assumption of an infinite plane. It was found that the errors caused due to the simplified plane wave assumption can be significant, especially for hard surfaces and near grazing incidence. As something new, the gradual transition from a spherical wave to a plane wave approximation was addressed. For sound propagation exceeding 50 times the wavelength, the plane wave approximation was found to be fully justified, but for shorter distances the spherical wave reflection model should be applied. In contrast to previous work on spherical wave reflection, the reflection from a finite-sized surface was studied. For the first time, the spherical wave reflection model was combined with the complex radiation impedance of a finite-sized surface. One interesting application example of the spherical reflection model is the attenuation of sound propagation above the audience area in a performance space. Finally, the extension of the spherical wave reflection model to higher order reflections was addressed. Full article

Unlike reactive systems, mechanical ventilation controlled by CO₂ concentration operates at a target efficiency that dynamically increases whenever the target CO₂ level is exceeded. This approach eliminates the typical ‘dead-time’ and prevents air quality degradation by ensuring the system adjusts its performance immediately in response to concentration changes. In this work, the study focuses on the development and evaluation of data-driven predictive models for near-term indoor CO₂ forecasting that can be integrated into pre-occupancy ventilation strategies, rather than designing a complete control scheme. Experimental data were collected over four months in a 48 m² smart laboratory configured as an open-plan office, where a heterogeneous IoT sensing architecture logged synchronized time-series measurements of CO₂ and microclimate variables (temperature, relative humidity, PM_2.5, TVOCs), together with acoustic noise levels and appliance-level energy consumption used as indirect occupancy-related signals. Raw telemetry was transformed into a 22-feature state vector using a structured feature engineering method incorporating z-score standardization, cyclic time encodings, multi-horizon CO₂ lags, rolling statistics, momentum features, and non-linear interactions to represent temporal autocorrelation and daily periodicity. The study benchmarks multiple regression paradigms, including simple baselines and ensemble methods, and found that an automated multi-level stacked ensemble achieved the highest predictive fidelity for short-term forecasting, with an Mean Absolute Error (MAE) of 32.97 ppm across an observed CO₂ range of 403–2305 ppm, representing improvements of approximately 24% and 43% over Linear Regression and K-Nearest Neighbors (KNN), respectively. Temporal diagnostics showed strong phase alignment with observed CO₂ rises during occupancy transitions and statistically reliable prediction intervals. Five-fold walk-forward cross-validation confirmed the temporal stability of these results, with top models achieving consistent R² values of 0.93–0.95 across Folds 2–5. These results demonstrate that, within a single-room university laboratory setting, historical sensor data from low-cost IoT devices can support accurate short-term CO₂ forecasting, providing a predictive layer that could support future proactive ventilation scheduling aimed at reducing CO₂ lag at the start of occupancy while avoiding unnecessary ventilation runtime. Generalization to other building types and occupancy profiles requires further validation. Full article

Diocletian’s palace with its cellars represents one of the most important cultural heritage sites of the ancient Roman civilisation on the present-day Croatian territory. The cellar complex has been rediscovered only recently and has been preserved remarkably well due to its centuries-long concealment beneath mediaeval urban matrices. An archaeoacoustic analysis was performed on a selected single-nave hall as a small part of this complex. A model of the hall was developed in room acoustics simulation software and calibrated based on the results of field measurements. Acoustic suitability of the hall for speech-based events and music performances was then evaluated according to contemporary objective criteria, and the findings were compared with the results of similar studies performed on other heritage sites. The hall was found to be very well suited for speech in terms of intelligibility and mid-frequency reverberation, thus showing potential for revitalisation, with excessive low-frequency reverberation in the hall and reduced audibility in the farthest part of the audience as potential issues. With a feasible audience size, the hall is not reverberant enough for music performances but provides high clarity. In terms of sound strength, the hall is suitable for solo performers or small ensembles. Excessive perceptive broadening of the sound source is expected due to strong early lateral energy. In terms of traditional Dalmatian a cappella singing, the acoustics of the hall are likely to support and enhance such performances. Full article