Search Results (142)

Speech perception relies on distributed neuronal populations, yet traditional decoding often utilizes static strategies that overlook inherent temporal dependencies and dynamic regulation. Therefore, we introduce the concept of system identification into multivariate decoding. By modeling brain response characteristics through time-lagged regression between speech stimuli and neural responses, we propose a temporal response function-based representational similarity analysis method (TRF-RSA). This method models the dynamic time-lag mapping from continuous stimulus features to neural responses, effectively separating stimulus-driven coherent activity from high-dimensional noise. More importantly, it elevates the analytical perspective from static comparisons of raw signals to dynamic trajectories in weight space. We conducted an auditory experiment and incorporated high spatiotemporal resolution optically pumped magnetometer magnetoencephalography magnetoencephalography (OPM-MEG) with electroencephalography (EEG). The results showed that TRF-RSA significantly enhanced the pattern similarity between speech sounds and the ability to discriminate between pattern differences. Furthermore, it revealed stronger similarities elicited by biological vocalizations, indicating a preference in the brain for these species-specific sounds. Source localization results not only confirmed the classical speech perception network but also revealed activation in limbic and deep brain regions. By modeling the relationship between stimulus features and neural responses, TRF-RSA dynamically quantified the spatiotemporal patterns of stimulus-driven neural activity, improving the sensitivity of representational pattern decoding during the encoding process. These findings suggest that this method is a sensitive neuroimaging tool that not only advances our understanding of the spatiotemporal dynamics of speech processing but also provides a new reference for population dynamics research. Full article

Accurate channel state information is essential for the performance of modern wireless communication systems. Conventional channel estimation typically relies on uplink Sounding Reference Signals (SRSs), which can introduce considerable overhead and power consumption, particularly in high-mobility or resource-constrained scenarios. To alleviate this burden, this paper explores an alternative approach that leverages sensing channel information to assist communication channel reconstruction. A shared cluster concept is introduced to capture the correlation between sensing and communication channels, and a sharing probability function is derived through statistical analysis of ray tracing simulation data across multiple scenarios. The shared cluster parameters extracted from the sensing channels are integrated into a cluster-based channel modeling framework to reconstruct the downlink communication channel. A deterministic simulation platform is developed using the Sionna ray tracing library, and the K-Power-Means algorithm is employed for multipath clustering. Simulation results demonstrate that the reconstructed channel closely matches the original channel in terms of the power delay profile and the root mean square delay spread, with mean values of 84.16 ns and 73.52 ns, respectively. The proposed method offers a promising supplementary approach for channel acquisition in scenarios where frequent SRS transmission is undesirable, and provides insights for future sensing-assisted communication system design. Full article

High sound pressure level (SPL) acoustic sensing requires miniaturized microphones that can operate under large acoustic loading while maintaining mechanical linearity, sufficient sensing response, and broadband audio frequency behavior. This work targets high-SPL operation and numerically investigates a graphene piezoresistive MEMS microphone based on a membrane-supported beam–island diaphragm. The proposed structure retains a continuous membrane for acoustic load bearing, while the upper beam–island topology redirects deformation-induced strain toward beam root regions where graphene piezoresistors are placed. This design is intended to increase the local strain available for piezoresistive readout without simply relying on larger global diaphragm deflection. Finite-element analysis was used to optimize the diaphragm geometry and evaluate strain enhancement, pressure response linearity, modal behavior, and harmonic response. Under the 170 dB SPL reference condition, the optimized structure increases the peak structural strain from 47.83 με in a thickness-equivalent solid diaphragm to 562.53 με, achieving an approximately 11.8-fold enhancement in local sensing strain while maintaining a highly linear pressure response (R2 > 0.9999). Additionally, the results also show that the sensor exhibits a high first natural frequency of 64.07 kHz and a small response variation of approximately 0.94 dB within the 0–20 kHz target frequency range, indicating excellent dynamic stability and high-fidelity signal transduction characteristics. To connect the structural response with piezoresistive readout, first-order electromechanical output estimation was further performed using representative graphene gauge factors, quarter-bridge readout assumptions, contact resistance correction, and Johnson-noise-limited signal-to-noise ratio estimation. A ±5% geometric tolerance check further indicates that the membrane side length is the most fabrication-sensitive parameter, while the selected design remains generally robust except for reduced linearity margin under positive membrane side-length deviation. These results demonstrate the potential of the proposed graphene-based MEMS microphone for high-SPL broadband acoustic sensing applications in harsh and high-intensity acoustic environments. Full article

This systematic review summarizes experimental studies in atmospheric acoustics that quantify environmental influences on long-range sound propagation. A keyword-based search was conducted in Scopus and Google Scholar to identify relevant records. Studies were included if they were published in English between January 1977 and April 2026, investigated long-range sound propagation within the human audibility range using specific sound sources, and incorporated concurrent meteorological measurements. Two reviewers worked independently to assess eligibility of the studies included in this review. Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, this systematic review surveys the methodological content of these studies with respect to sound sources, signal content and processing, microphone configuration, treatment of the ground and topography, and meteorological measurements to identify common practices. Some studies provide only limited information about the acoustic source properties, postprocessing of acoustic data, and/or configuration of meteorological measurements. Key experimental details for the 40 included studies are tabulated and summarized via histograms for reference. Most experimental acoustic studies have measured propagation within a range of 2 km on relatively flat land and have utilized tower-based meteorological measurements. The results of the studies surveyed here have implications for understanding long-range outdoor sound propagation, including development of accurate numerical models. Some contributing authors were funded by the Office of Naval Research: ONR Award N00014 24-1-2400, ONR Award N00014-24-1-2437. Full article

Acoustic behavior, essential for communication and perception, is metabolically demanding. Studying the energy costs of echolocation helps us to understand animal energy allocation and provides key insights into the evolutionary constraints of acoustic signals. We examined the constant-frequency bat Rhinolophus nippon using a miniature electrocardiogram system and a custom servomotor that moved prey toward stationary bats. This setup allowed for synchronous recording of high-resolution electrocardiogram and echolocation calls from the search phase to the approach phase. During the search phase, bats emitted isolated echolocation pulses characterized by long pulse durations and inter-pulse intervals (IPIs), together with higher root mean square (RMS) amplitude, pulse energy, and peak amplitude. In the approach phase, call rate increased significantly (3.15-fold), and bats predominantly produced sonar sound groups. Meanwhile, pulse duration, IPIs, RMS amplitude, and pulse energy decreased to 65.23%, 25.82%, 78.50%, and 86.32% of the corresponding search-phase values, whereas peak amplitude increased to 110.99%, indicating that R. nippon can flexibly adjust the structure of its echolocation calls. However, despite the increased call rate (p < 0.05), neither heart rate nor metabolic rate differed between phases. This study provides direct physiological evidence for understanding energy expenditure in bat echolocation and offers a methodological reference for future research. Full article

Accurate and reliable groundwater-level monitoring in deep observation wells remains difficult for conventional non-contact ultrasonic systems because narrow tubular geometries intensify multipath reflections, signal attenuation, and echo ambiguity. This study proposes a dual-signal direct time-of-flight (ToF) method that combines radiofrequency (RF) synchronization with one-way airborne ultrasonic propagation to a floating receiver located at the groundwater surface. In the proposed architecture, the RF signal provides a near-instantaneous time reference, whereas the ultrasonic signal defines the propagation delay, thereby eliminating dependence on echo-based ranging. The system integrates a wellhead surface unit for synchronized transmission and control, a floating unit for ToF acquisition and embedded processing, and an optional reference channel for in situ estimation of the effective sound speed. A duty-cycled power architecture is used to support low-power long-term deployment, while a multi-shot acquisition strategy with a median-like estimator improves robustness against startup transients, timing jitters, and false detections. Field validation was conducted over a 12-month period under actual groundwater-monitoring conditions, during which the groundwater depth varied between 14 m and 30 m below the wellhead datum. Within this field-validation interval, the proposed system achieved a mean absolute error of 0.048 m, a maximum absolute error of 0.050 m, and an overall valid detection rate of 99.4% over 358 valid cycles out of 360 scheduled cycles. In addition, a separate range-dependent confined-tubular propagation test was conducted to evaluate the extended detection capability of the RF-synchronized one-way ultrasonic ToF architecture. This test demonstrated stable acoustic-link ToF detection up to 300 m inside the tested 170 mm confined plastic pipeline. Therefore, the 300 m result should be interpreted as a range-dependent valid-detection result rather than as a 12-month groundwater-depth validation over the full 300 m interval. These results demonstrate that the proposed direct-ToF method provides an RF-synchronized one-way ultrasonic ToF framework with a floating receiver for groundwater-level monitoring in deep observation wells, while remaining compatible with low-power and IoT-based environmental monitoring systems. Full article

A substantial share of reinforced-concrete infrastructure assets has reached an age where deterioration mechanisms such as cracking, delamination, and voiding may develop, potentially increasing safety risks and maintenance demands. Conventional condition assessment commonly relies on localized intrusive testing (e.g., coring) and manual sounding, which can be disruptive, labor-intensive, and partly subjective. Vibration-based Non-Destructive Testing (NDT) provides an alternative by exciting the structure and evaluating changes in its dynamic response. In contrast to previous studies, which typically assess a single excitation method in isolation, this study provides a systematic side-by-side comparison of three vibration-based NDT excitation approaches: mechanical impact using a custom compressed-air impact device, acoustic excitation, and shaker excitation. All three methods were evaluated under identical measurement conditions. The vibration response is measured using Laser Doppler Vibrometry (LDV), enabling non-contact acquisition of frequency-response signatures. A custom mechanical excitation device was developed and evaluated, and the results indicate that it provides stable and repeatable excitation with good defect discrimination. Experiments on specimens with representative defect types show that mechanical impact and shaker excitation yield the most repeatable and discriminative response features, whereas acoustic excitation provides insufficient signal-to-noise ratios (SNRs) for the smallest tested specimens. Among the evaluated setups, the Qsources surface-mounted shaker and the compressed-air impact device provided the most promising laboratory results. However, the large electrodynamic shaker was used mainly as a controlled reference excitation method, and scalable field inspection would require more compact and automated excitation solutions. The goal of this work is therefore to support the development of efficient LDV-based non-contact inspection methods for safer and more reliable monitoring of reinforced-concrete infrastructure. Full article

Detecting transient “click” sounds during connector insertion is pivotal for automotive assembly quality but remains intractable due to high-intensity, non-stationary industrial noise. This paper introduces a physics-aware generative demasking framework that integrates acoustic spatial priors with conditional diffusion modeling. We propose the spatially conditioned diffusion probabilistic model (SC-DPM), where an ambient reference signal acts as a physical constraint to steer the reverse diffusion process. By exploiting the spatial decay of insertion sounds, this mechanism effectively disentangles the target transient from the background noise manifold, reconstructing high-fidelity spectro-temporal features. Discriminative temporal patterns are extracted using causal random convolutional kernels with causal dilations and local proportion of positive values (LPPV) pooling. Experiments on real-world datasets demonstrate 93.3% accuracy. The proposed “restore-then-classify” paradigm significantly enhances robustness against acoustic variability, establishing a scalable methodology for precise industrial monitoring under extreme noise conditions. Full article

Swallowing is a critical marker of neurological and emotional health. The ability to monitor it continuously and non-invasively, especially through smart ear-worn devices, holds significant promise for clinical applications. Despite this potential, no public audio datasets currently support reliable swallowing sound detection. Existing datasets focus primarily on speech and breathing, offering limited coverage and lacking detailed annotations for swallowing events. To address this gap, we introduce an in-ear audio dataset specifically designed to capture a wide range of verbal and non-verbal sounds. It includes comprehensive labeling focused on swallowing. The dataset was collected from 34 healthy adults (14 females and 20 males) between the ages of 20 and 29. Each participant performed a series of predefined tasks involving both non-verbal and verbal events. Non-verbal tasks included swallowing, clicking, forceful blinking, touching the scalp, and physical movements such as squatting or walking in place. Verbal tasks consisted of speaking (e.g., describing an image). Recordings were conducted in both quiet and noisy environments to better reflect real-world conditions. Data were captured using a combination of in-/outer-ear microphones, a chest belt to record electrocardiogram (ECG), respiration and acceleration signals, and an ultrasound probe to track tongue movement, which served as a reference for swallowing annotation. All signals were precisely synchronized. To ensure high data quality, the recordings were reviewed using both algorithmic analysis and manual inspection. Swallowing events were identified based on ultrasound signals and validated by an expert to guarantee accurate labeling. As a proof of concept that in-ear audio supports swallow classification, we fine-tune a fully connected neural network on YAMNet embeddings plus zero-crossing rate (ZCR) features. Across the completed folds, the model reaches an F1 score of 0.875 ± 0.013. Full article

Heart and respiratory rate monitoring during sleep enables the detection of physiological irregularities through contact or contactless methods. Traditional approaches like polysomnography are accurate but costly, ergonomically limited, and often poorly accepted by patients. Smart Bedding^® is a novel, flexible bedsheet equipped with a high-resolution sensor network that records movement, pressure, sound, temperature, and humidity throughout the night. This study aimed to estimate cardiorespiratory parameters using the Smart Bedding^® IMU. Data from 30 participants sleeping on Smart Bedding^® while undergoing simultaneous polysomnography were analyzed. A robust and low-cost preprocessing pipeline was developed; estimation was performed using zero-crossing, peak detection, and Burg’s method for comparison, and validation was conducted using polysomnography as the gold-standard reference. Respiratory and heart rates were accurately estimated, achieving overall accuracies of 93.9% and 88.7% using zero-crossing and peak detection, respectively. Respiratory rate estimation showed no significant limitations across the frequency spectrum or among sleeping positions. However, heart rate estimation accuracy decreased when the frequency was below 55 BPM or when participants slept in a lateral sleep position, likely due to reduced cardiac signal power. Overall, the proposed methodology accurately tracked respiratory and cardiac patterns throughout the night, supporting Smart Bedding^® as a promising tool for future sleep tracking applications. Full article

Sound reproduction is the electro-mechanical re-creation of sound waves using analogue and digital audio equipment. Although sound reproduction implies that repeated acoustical events are close to identical, numerous fixed and variable conditions affect the acoustic result. To arrive at a better understanding of the magnitude of deviations in sound reproduction, amplitude deviation and phase distortion of a sound signal were measured at various reproduction stages and compared under a set of controlled acoustical conditions, one condition being the presence of a human subject in the acoustic test environment. Deviations in electroacoustic reproduction were smaller than ±0.2 dB amplitude and ±3 degrees phase shift when comparing trials recorded on the same day (Δt < 8 h, mean uncertainty $u$ = 1.58%). Deviations increased significantly with greater than two times the amplitude and three times the phase shift when comparing trials recorded on different days (Δt > 16 h, $u$ = 4.63%). Deviations further increased significantly with greater than 15 times the amplitude and the phase shift when a human subject was present in the acoustic environment ($u$ = 24.64%). For the first time, this study shows that the human body does not merely absorb but can also cause amplification of sound energy. The degree of attenuation or amplification per frequency shows complex variance depending on the type of reproduction and the subject, indicating a nonlinear dynamic interaction. The findings of this study may serve as a reference to update acoustical standards and improve accuracy and reliability of sound reproduction and its application in measurements, diagnostics and therapeutic methods. Full article

Immersive methods and biometric tools provide a rigorous, context-rich way to study how people perceive and choose food. Immersive methods use extended reality, including virtual, augmented, mixed, and augmented virtual environments, to recreate settings such as homes, shops, and restaurants. They increase participants’ sense of presence and the ecological validity (realism of conditions) of experiments, while still tightly controlling sensory and social cues like lighting, sound, and surroundings. Biometric tools record objective signals linked to attention, emotion, and cognitive load via sensors such as eye-tracking, galvanic skin response (GSR), heart rate (and variability), facial electromyography, electroencephalography, and functional near-infrared spectroscopy. Researchers align stimuli presentation, gaze, and physiology on a common temporal reference and link these data to outcomes like liking, choice, or willingness-to-buy. This approach reveals implicit responses that self-reports may miss, clarifies how changes in context shift perception, and improves predictive power. It enables faster, lower-risk product and packaging development, better-informed labeling and retail design, and more targeted nutrition and health communication. Good practices emphasize careful system calibration, adequate statistical power, participant comfort and safety, robust data protection, and transparent analysis. In food science and consumer behavior, combining immersive environments with biometrics yields valid, reproducible evidence about what captures attention, creates value, and drives food choice. Full article

Blood pressure (BP) monitoring is essential for cardiovascular health management, yet traditional cuff-based methods face limitations including patient discomfort and inapplicability for certain populations. This study presents a deep learning framework for cuffless BP estimation using phonocardiogram (PCG) signals. The proposed model integrates convolutional neural networks (CNNs) with Squeeze-and-Excitation (SE) blocks and demographic information to enhance prediction accuracy. Mel-Frequency Cepstral Coefficients (MFCCs), along with their delta and delta–delta coefficients, were employed to capture comprehensive acoustic characteristics of heart sounds. The results demonstrated that the proposed model achieved high predictive accuracy and strong consistency with reference BP measurements. Component analysis confirmed that the inclusion of SE blocks provided substantial performance gains, while demographic information further improved prediction stability. Clinical validation also verified that the model maintained close agreement with true BP values across the tested population, showing significant improvement over the baseline CNN implementation. These findings suggest potential for accessible, non-invasive BP monitoring systems suitable for continuous health tracking. Full article

This paper examines how the fractional Fourier transform (FrFT) can be used to form and analyze acoustic holograms produced by a moving, linear, frequency-modulated (LFM) source in a shallow water waveguide. In these environments, the source sound field creates an interference pattern, referred to as a two-dimensional interferogram, which represents the distribution of acoustic intensity in the frequency–time domain. This interferogram consists of parallel interference fringes. Consequently, focal points are formed and aligned along a straight line in the source hologram, which is represented by the two-dimensional Fourier transform of the interferogram. We have developed a holographic method for constructing the interferogram of an LFM source signal and transforming it into a Fourier hologram based on FrFT in the presence of strong noise. A key finding of this study is that the FrFT-based holographic method enables localized focal regions to emerge from modal interference even under high-intensity noise conditions. The positions of these focal spots are directly related to the source parameters, enabling the estimation of key characteristics such as the distance and velocity of the LFM source. We analyzed the effectiveness of the FrFT-based holographic method through numerical experiments in the 100–150 Hz frequency band. The results demonstrate the method’s high noise immunity for source localization in realistic shallow water environments under strong noise. Full article

Aim: The aim of this study was to develop and test a surface electromyography (sEMG) assessment protocol to characterise the activity of the extrinsic laryngeal muscles (suprahyoid and infrahyoid) during phonatory tasks and vocal techniques. Methodology: The protocol of assessment was based on electromyographic assessment guidelines and on clinical voice evaluation needs and was tested in six healthy adults with no vocal disorders. Surface electromyographic activity of suprahyoid and infrahyoid muscles was acquired during different reference tasks (rest, reading, maximum contractions) and six vocal tasks, including nasal sounds, fricatives, and semi-occluded vocal tract exercises. A laryngeal accelerometer was used for detecting the beginning and end of each exercise. The average activity during each task was normalised by the signal obtained in the incomplete swallowing task for the SHM and by the sniff technique for the IHM. Results: The range of activation values varied across tasks, with higher percentages observed in plosive production and in the “spaghetti” technique, while nasal and fricative sounds tended to show lower activation values within the group. A consistent pattern of simultaneous activation of suprahyoid and infrahyoid muscles was observed during phonation. Conclusions: The protocol proved potential for clinical application in speech–language pathology as it enabled the characterisation of muscle activity in determinant muscles for vocal function. Larger samples and further validation of the time-marking system are needed. This study provides a foundation for integrating sEMG measures into functional voice assessment. Full article