Search Results (163)

Naturally ventilated office spaces present a dual challenge: open windows are sources of thermal comfort and acoustic disturbances. Despite growing interest in adaptive comfort frameworks, the acoustic adaptation effects specific to naturally ventilated indoor environments and the role of biophilic design elements in mediating them remain insufficiently investigated. This study was conducted to examine the influence of audio–biophilic elements in a naturally ventilated space with a green view, with 53 participants recruited from a real, operational open-office space. Under typical open window field noise conditions, four levels of birdsong (45, 49, 52, and 55 dBA) were introduced at the receiver position. The participants’ perceptual responses were measured using the ISO/TS 12913:2019 and ISO/TS 12913:2025, as well as indoor-soundscape scales. Satisfaction, the perceived appropriateness of the conditions for work, and preferences were evaluated. Environmental descriptors, including psychoacoustic and thermal parameters and perceived auditory and visual content, were also recorded. Statistical analyses were conducted using RM-ANOVA, the Friedman test, and post hoc comparisons. The results revealed that audio–biophilic interventions can enhance adaptive acoustic comfort in naturally ventilated spaces. Although the existing ISO and indoor soundscape scales are highly correlated, they are not interchangeable. These findings offer actionable guidance for acoustic designers and facility managers: introducing contextually appropriate birdsong at moderate levels (not exceeding a background noise level of more than 3 dBA) can serve as an effective masking strategy in naturally ventilated open-plan offices without increasing perceived disturbance, supporting the integration of audio–biophilic systems in green building design. This study contributes field-based evidence to the literature on audio–biophilic interventions and their role in adaptive acoustic comfort in naturally ventilated spaces. Full article

Urban waterfronts are vital public spaces that contribute to urban sustainability by providing residents with opportunities for recreation, social interaction, and nature experiences. Understanding user perceptions in these environments is essential for evidence-based design. Taking Taiyuan Fenhe Park in China as a case with local residents as respondents, this study investigated how objective audiovisual characteristics are associated with tourist loyalty through perceptual dimensions, while also examining interactive associations between visual and auditory elements. Data were collected at 539 spatial samples spaced at five-minute walking intervals. Methods included on-site acoustic measurements, panoramic imaging, computer-based visual and auditory quantification, and questionnaire surveys, yielding a total of 1768 valid responses. Visual features were quantified using semantic segmentation, object detection, and depth prediction, whereas the auditory environment was characterized by physical acoustic and psychoacoustic indicators. Three perceptual dimensions—environmental restorativeness (ERS), spatial vitality (SVS), and environmental controllability (ECS)—were extracted and tested as mediators within the stimulus–organism–response (S-O-R) framework. Results indicated that ERS, SVS, and ECS function as three parallel mediating constructs in the statistical model, with SVS showing the strongest statistical association with tourist loyalty. In addition, fluctuation strength exhibited a significant direct effect on tourist loyalty independent of these three perceptual dimensions. A total of 17 significant audiovisual interactions were identified, revealing both synergistic and antagonistic effects. These findings contribute to theoretical frameworks of multisensory integration and provide practical guidance for sustainable waterfront design. Specifically, zoning strategies and carefully selected audiovisual combinations are relevant to enhanced user experience and may contribute to long-term social well-being. Full article

Electric vehicles (EVs) have fundamentally changed the noise, vibration, and harshness (NVH) landscape of automotive powertrains. In the absence of masking internal-combustion-engine noise, harmonic components such as gear whine, electric-motor orders, and inverter-related tones become more perceptible and more critical to vehicle refinement. This review synthesizes the current state of the art in harmonic NVH engineering for electric drivetrains, focusing on the interactions between gear geometry, manufacturing variability, electromechanical coupling, structural transfer, and human sound perception. Classical mechanisms of gear-mesh excitation are revisited together with emerging EV-specific challenges, including long-wavelength flank deviations, ghost orders, lightweight housing dynamics, and psychoacoustic sound-quality requirements. The review further examines recent progress in predictive and data-driven approaches, including machine-learning-based gear-noise modeling, digital-twin concepts, and virtual NVH assessment workflows. Overall, the literature shows that harmonic NVH engineering in EVs is evolving from a conventional gear-noise problem into a multidisciplinary system-level task integrating gear dynamics, manufacturing science, structural acoustics, electric-drive control, psychoacoustics, and data-driven optimization. This review provides a structured synthesis of these developments and identifies key research gaps and future directions for the next generation of refined electric drivetrains. Full article

Noise in industrial buildings affects workers’ productivity and can seriously impair their physical and mental health, yet existing studies often overlook workers’ subjective perceptions and rely on a single method. Therefore, this study recruited 263 workers from four industrial buildings in Beijing and adopted a mixed-methods approach. First, 30 semi-structured interviews were analyzed using grounded theory’s three-level coding procedure to construct a conceptual framework of a healthy acoustic environment and its influencing factors. Next, a 30-item subjective questionnaire was developed, and structural equation modeling was conducted on 256 valid responses. Finally, Spearman correlation analysis and multidimensional scaling were used to examine relationships between subjective evaluations and eight physical and psychoacoustic indicators. The results identified nine major dimensions, including Sound Source Localization, Physiological Effects at Work, and Regulatory Control, as well as 15 relational pathways. Compared with existing frameworks, Communication Barrier emerged as a more prominent dimension in industrial building contexts. Structural equation modeling confirmed that 12 pathways were statistically significant. Correlation analysis further showed that only a few objective–subjective associations were significant, indicating that objective acoustic indicators alone cannot explain workers’ multidimensional perceptions. In conclusion, this study developed an evaluation model for healthy acoustic environments in industrial buildings, highlighting the need to emphasize controllability, communication support, and integrated subjective–objective evaluation in acoustic design to better enhance workers’ well-being. Full article

This paper examines the subjective annoyance associated with shared-bike electronic alert sounds (SBeASs), an emerging urban noise source. A study was conducted by employing extensive questionnaire surveys and psychoacoustic experiments. A preliminary survey (N = 1340) indicated that 90.6% of participants reported being impacted by SBeASs, with pronounced effects on nighttime rest and daytime work efficiency. In this study, SBeAS samples were taken from three prominent Chinese bike-sharing brands: Hello Bike, Meituan Bike, and DiDi Bike. Under laboratory conditions, subjective annoyance assessments (N = 28) for SBeASs were conducted at controlled sound pressure levels (SPLs) ranging from 45 to 65 dBA, with occurrence frequencies of 1, 3, and 5 s. Simultaneously, annoyance assessments were also conducted for two reference noise types: traffic noise and street noise. The results indicated a notable increase in annoyance levels related to SBeASs with rising SPL and increased occurrence frequency. Minor variations in annoyance were identified among different bike-sharing brands, which can be attributed to their distinct acoustic features. When the SPL was above 55 dBA, the DiDi Bike SBeASs produced considerably higher annoyance than those of other brands. This can be attributed to its elevated low-frequency energy, loudness, and roughness. Moreover, individuals exhibiting increased sensitivity to noise reported notably higher annoyance ratings on the SBeAS scale (p = 0.019). Under low-SPL conditions (45–55 dBA), the annoyance attributed to frequent SBeASs can exceed that caused by traffic noise and street noise at comparable SPLs, highlighting the distinct disruptive impact of abrupt sound sources. Full article

Drone noise-induced human annoyance is emerging as one of the main barriers to socially acceptable large-scale urban air mobility (UAM) operations, which have the potential to revolutionize urban transportation systems in the next few decades. This paper investigates the state-of-the-art technology in the assessment of drone noise and its impact on individuals, focusing on measurement and evaluation methodologies, as well as subjective evaluations. Various acoustic metrics are reviewed to characterize drone noise, including sound pressure levels, spectral analysis, and psychoacoustic parameters such as loudness and annoyance. Preliminary experimental investigations to identify key frequencies and tonal components that significantly contribute to drone noise-induced public annoyance are also discussed. Interdisciplinary approaches integrating pure technical acoustics, human perception, and subjectivity emerge as promising solutions for a comprehensive understanding of drone noise effects. Finally, a preliminary framework for drone noise assessment towards noise-aware UAM operations is proposed. Full article

This study investigates the aerodynamic, aeroacoustic, and psychoacoustic behaviour of a side-by-side twin-propeller Unmanned Aerial Vehicle (UAV) system operating under both static and forward-flight conditions, with particular focus on the effects of asynchronous rotational speeds. Experiments were conducted using two identical five-bladed constant pitch propellers with a diameter of 9 in (228.6 mm) and a pitch to diameter ratio of 1. Rotational speed differences between 0 and 300 rpm were examined in 50 rpm increments at inflow velocities of 0 m/s, 14 m/s and 24 m/s. The results show that variations in rotational speed have a significant influence on both acoustic levels and perceived annoyance. Asynchronous operation causes the dominant tonal peak at the blade passing frequency to split into two components, reducing tonal reinforcement. This produces noise level reductions of approximately 2 dB in static and high advance ratio conditions, increasing to about 5 dB reduction at low advance ratios. Psychoacoustic metrics show greater sensitivity to tonal structure than to overall sound pressure level, with annoyance reductions of about 5% in static conditions and up to 15% at low advance ratios. A modest aerodynamic penalty of about 5% at $\Delta N=50$ rpm is observed, increasing with larger speed mismatches. Full article

In order to provide an innovative form of urban air mobility, a new and versatile generation of small, highly automated aircraft is currently being developed. This is made feasible by the development of new technologies such as electrified powertrains, Vertical Take-Off and Landing capabilities and distributed propulsion systems. The operation of these novel aircraft types will generate a new source of air traffic noise. In particular, the perception of noise and the annoyance caused by these aircraft and their distributed propulsion systems are likely to deviate from those of conventional aircraft and will also depend on psychoacoustic effects. Thus, the noise emission and its subjective perception will be key factors for the success of urban air mobility vehicles and their acceptance by society. In order to investigate acoustic effects that enable low-noise aircraft design, a multidisciplinary approach is applied to develop new aircraft concepts for urban air mobility. This approach includes the conceptual design of two vehicles, one vehicle with tilt-rotors and one with tiltable, ducted fans; the sizing of an electric powertrain; the design and manufacturing of a wingtip rotor; and the design and manufacturing of the low-speed ducted fans. This paper presents the design of the two vehicle architectures, including their electric powertrain, as well as the aerodynamic and acoustic performance of the rotor and fan. Full article

Aircraft noise monitoring systems deployed at major airports typically rely on scalar energy-based indicators, which primarily describe integrated sound energy but provide limited representation of the spectral–temporal structure and perceptual attributes of aircraft noise. To address this limitation, this study proposes a sensor-based psychoacoustic feature extraction and spatiotemporal analysis framework for continuous aircraft noise monitoring under high-density operational conditions. An automatic noise monitoring system compliant with ISO 20906 was deployed to synchronously acquire acoustic waveforms and ADS-B trajectory data. A cascaded spatiotemporal fusion algorithm was developed to associate noise events with aircraft flight paths, followed by a model-stratified multidimensional IQR-based data cleaning strategy to suppress environmental interference and non-stationary outliers. Based on the cleaned dataset, a suite of psychoacoustic features—including loudness, sharpness, roughness, fluctuation strength, and tonality—was extracted to characterize the perceptual structure of aircraft noise beyond conventional energy metrics. Experimental results demonstrate that, under equivalent sound exposure levels, psychoacoustic features retain substantial discriminative information that is lost in scalar energy indicators. The coefficients of variation for fluctuation strength and tonality reach 43.2% and 22.1%, respectively, corresponding to 15–69 times higher sensitivity compared to traditional energy-based metrics. Furthermore, nonlinear manifold mapping using UMAP reveals clear topological separation between new-generation and legacy aircraft models in the psychoacoustic feature space, whereas severe overlap persists in energy-based representations. Correlation analysis further indicates decoupling between macro-level physical design parameters (e.g., bypass ratio, thrust) and perceptual feature dimensions, highlighting the limitations of energy-centric monitoring schemes. The proposed framework demonstrates the feasibility of integrating psychoacoustic feature extraction into continuous sensor-based aircraft noise monitoring systems. It provides a scalable signal processing pipeline for enhancing the resolution and interpretability of aircraft noise measurements in complex operational environments. Full article

In modern buildings, the air-conditioned indoor environment is vital for occupant productivity and well-being, yet fan noise and airflow turbulence can significantly compromise these benefits. Human–environmental interactions are complex processes that traditional energy-based acoustic metrics are often insufficient to model. Therefore, this study aims to advance the multidimensional sound quality assessment framework for building acoustics. Three methods, the conventional regression approach (CRA), general prediction model (GPM), and psychoacoustic machine learning (PML) assessment methods, were evaluated for predicting three perceptual dimensions (Evaluation, Potency, Activity; EPA) and negative noise impacts on occupant well-being (O1: Discomfortable, O2: Annoying, O3: Stressful, and O4: Unacceptable). Based on 432 multidimensional sound quality assessments across four general types of air-conditioned built environments, the PML achieved the best goodness-of-fit for the EPA-score perdition (adjusted R² = 0.61) compared to CRA (0.32) and GPM (0.15) and effectively predicted all negative noise impacts (adjusted R² = 0.53–0.61). The PML assessment method offers a smart and reliable solution for sound quality and well-being prediction through psychoacoustic heatmaps encoding time-varying psychoacoustic features in 227 × 227 pixels from 30 s soundtracks of the built environment for sustainable building design. Full article

Over the past four decades, tinnitus research has grown into a highly interdisciplinary field spanning auditory science, neuroscience, psychology, and clinical medicine. Yet how knowledge across subfields has been inherited, diversified, and integrated over time still lacks traceable structural evidence. To address this gap and move beyond frequency-oriented reviews and bibliometric studies that mainly report “hot topics” and prolific contributors, the present study reconstructs the intellectual evolution of tinnitus research (1984–2025) using citation-network-based main path analysis (MPA). From the Web of Science Core Collection, 6584 records were initially retrieved, of which 6354 formed a mutually linked core citation network (96.5%), indicating high coverage and analyzability. SPLC (Search Path Link Count)–weighted MPA was applied to extract global and key-route main paths capturing dominant knowledge trajectories and major branches. Cluster and co-word analyses were then integrated to delineate seven evolutionary stages and five major thematic clusters. This framework identifies bridging works and turning points and reveals how emerging lines—neuromodulation, implant-related treatments, and digital/telehealth CBT—branch from and later converge with established neurobiological and psychological pathways rather than appearing in isolation. Overall, the field has progressed from early psychoacoustics and spontaneous otoacoustic emissions through cochlear-injury plasticity, central gain, and limbic–auditory network models, and most recently toward mechanism-oriented diagnostics, individualized assessment, and targeted interventions. Full article

This study investigates how listeners perceive consonance and dissonance in dyads composed of simple (sine) tones, focusing on the effects of frequency ratio (R) and mean frequency (F). Seventy adult participants—categorized by musical training, gender, and age group—rated randomly ordered dyads using binary preference responses (“like” or “dislike”). Dyads represented standard Western intervals but were constructed with sine tones rather than musical notes, preserving interval ratios while varying absolute pitch. Statistical analyses reveal a consistent decrease in preference with increasing mean frequency, regardless of interval class or participant group. Octaves, fifths, fourths, and sixths showed a nearly linear decline in preference with increasing F. Major seconds were among the least preferred. Musicians rated octaves and certain consonant intervals more positively than non-musicians, while gender and age groups exhibited different sensitivity to high frequencies. The findings suggest that both interval structure and pitch range shape the perception of consonance in simple-tone dyads, with possible psychoacoustic explanations involving frequency sensitivity and auditory fatigue at higher frequencies. Full article

Air-conditioning systems are vital for indoor environmental quality. However, noise can offset its benefits, making acoustic monitoring important. Recent research revealed that sound quality perceptions can be described by three psychological dimensions: Evaluation, Potency, and Activity (EPA). This is the first study to develop psychoacoustic heatmap machine learning models (PHMLM) for predicting sound quality and the negative noise impacts (O1: Discomfortable, O2: Annoying, O3: Stressful, and O4: Unacceptable) of air conditioning sounds using a 227 × 227-pixel psychoacoustic heatmap as input for machine learning. A total of 1208 jury listening tests were conducted with 101 participants on 30 s soundtracks from air-conditioned environments. Psychoacoustic heatmaps were generated by converting time-varying psychoacoustic metrics (N, S, R, and FS) into intensity maps containing 51,529 pixels of multidimensional acoustic information. The PHMLMs achieved predictive performance with correlation coefficients of 0.79, 0.80, and 0.62 for E-, P-, and A-scores, respectively. Compared to traditional regression models (TRM), PHMLM-EPA demonstrated significantly better performance with 31% lower mean absolute error (4.4 vs. 6.4) and higher regression slope (0.798 vs. 0.587). Moreover, PHMLM-EPA demonstrated a higher goodness-of-fit than TRM (+55% to +95%) and traditional acoustic metric L_Aeq (+87% to +95%). The approach offers an advanced acoustic monitoring method for sustainable building designs. Full article

Noise regulations often apply penalties (e.g., +5 dB) to A-weighted equivalent sound pressure levels ($L_{\text{Aeq}}$ [dB]) to account for increased annoyance from tonal or impulsive features. Psychoacoustic evidence indicates that spectral characteristics also affect annoyance, with some spectra being substantially more disturbing than others. Yet, no established method exists for determining spectrum-based penalties from measured sound spectra. This study aimed to develop a simple, objective model for assigning penalties to steady-state broadband sounds based on spectral properties. Using experimental data comprising annoyance ratings and penalties for 23 spectrally distinct broadband sounds at three $L_{\text{Aeq}}$ levels (32, 40, and 48 dB), we evaluated several single-number noise descriptors from the literature. Room The Noise Criterion showed the strongest association with direct annoyance ratings, while the spectral centroid (SC) and sharpness were most closely related to spectrum-based penalties. Due to its simplicity, the spectral centroid was selected for the final model: $k=6.9·{\text{log}}_{10}\left(\mathit{SC}\right)-16.3$. The proposed model is expected to be applicable for broadband sounds within 32–48 dB $L_{\text{Aeq}}$ and offers a practical approach for incorporating spectral effects into noise assessment. Full article

In this work, we tested the use of Convolutional Neural Networks (CNNs) to classify booming noise inside vehicles. Instead of relying only on long experimental campaigns, we generated a synthetic dataset from Sound Quality Equivalent (SQE) models that were originally built from real acoustic measurements collected with sensors. By applying smoothing functions and Hann windows, we were able to vary the intensity of the booming effect across different mission profiles. The CNNs were trained on spectrograms derived from these signals, with labels informed by psychoacoustic evaluations. The best model reached about 95.5% accuracy in the binary task (booming vs. no booming) and around 93.3% when using three classes (severe, mild, none). Tests with data from three different car models showed that the method can generalize across platforms. These results suggest that CNNs may become a practical tool for NVH analysis, offering a simpler and cheaper complement to traditional end-of-line testing, and one that could be adapted for real-time embedded systems. Full article