Search Results (328)

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

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

MEMS microphones inspired by the coupled ears of the fly Ormia ochracea have been extensively investigated for miniature, high-accuracy, and low-noise-floor sound source localization (SSL). However, most studies focus on the rocking-mode-dominated bidirectional polar response for SSL while neglecting the omnidirectional response of the bending mode, leaving other directional responses arising from the dual-mode superposition largely unexplored. Therefore, in this paper, based on a low-damping optical beam deflection (OBD) biomimetic microphone with a pronounced bending-mode omnidirectional response, various directional responses arising from the dual-mode superposition are identified and characterized. Both simulation and experimental results demonstrate that, under the dual-mode superposition, the directional responses of the OBD biomimetic microphone transition from the bidirectional polar pattern with asymmetric lobes to the two gradually overlapping circular patterns and eventually to the two nearly completely overlapping circular patterns, and this process is well described by the developed theoretical model. Moreover, we explore the SSL performance of the modal superposition-induced directional responses and demonstrate for the first time that the non-overlapping circular patterns have the same sinusoidal SSL potential as the bidirectional polar responses. This paper advances the understanding of modal superposition-induced directional responses and expands the variety of directional responses available for SSL in biomimetic microphones. Full article

Recent advances in eye-tracking technologies have fostered growing interest in their integration with acoustic research for investigating auditory perception and human behavioral responses. This study presents a structured literature review of recent developments at the intersection of eye tracking and acoustics, with the aim of analyzing how eye-movement data can support the interpretation of auditory events, spatial listening behaviors, and multimodal human–environment interactions. The reviewed studies were organized into four main research areas focusing on the application of eye-tracking in acoustics: sound source localization and identification, sound event detection and classification, acoustic sensing and multimodal systems, and soundscape and perceptual acoustic studies. The analysis indicates that eye-movement patterns can provide useful indicators of auditory attention and perceptual processes, particularly when combined with complementary physiological, visual, and acoustic sensing modalities. Furthermore, recent methodological advances, including real-time processing, machine learning algorithms, and sensor fusion techniques, have contributed to improving the robustness and accuracy of multimodal data analysis. Nevertheless, the review also highlights several limitations in current research, such as the lack of standardized experimental protocols, inter-individual variability, and susceptibility to environmental noise and external interference. Finally, future research perspectives are discussed, emphasizing the development of standardized and adaptive multimodal frameworks for human behavior modeling and intelligent acoustic monitoring systems. Full article

The deconvolution approach for the mapping of acoustic sources (DAMAS) based on the microphone array is proved effective in various acoustic imaging applications. Generally, DAMAS and its variations result in heavy computation load due to the nature of large-scale linear equations and the iterative solver, which prevent the deployment of DAMAS to platforms with limited resources, such as the edge devices of the internet of things (IoT). In order to enhance the computational efficiency of DAMAS, a fast algorithm based on DAMAS with grid compression by the morphological operations on the acoustic images is proposed in this work. The proposed approach intentionally neglects the physics behind the acoustic imaging, but emphasizes the general visual features of acoustic images, as if they were natural images. A low computation load can be guaranteed regardless of the complicated acoustic environments, which alternatively ensures the robustness of proposed algorithm. Numerical simulations demonstrate that the proposed algorithm effectively accelerates the acoustic image reconstruction. In practical experiments, the proposed method reduces the algorithm time to be within 26% of DAMAS. In certain scenarios, both the algorithm time and localization accuracy of the proposed method outperform the conventional methods. Full article

Accurate rotating sound source localization requires precise knowledge of the relative pose between the microphone array and the rotating object. In practice, six-degrees-of-freedom (6-DOF) installation uncertainties often arise, degrading beamforming performance. This paper presents a 6-DOF compensation method formulated as an optimization problem. The method treats the array as a rigid body and estimates its translational and rotational offsets by minimizing the sum of Euclidean distances between beamforming peaks and a small set of stationary single-frequency reference sources. A novel cascaded local-Bayesian optimization (CLBO) algorithm is proposed, initialized via coordinate descent local search followed by Bayesian optimization refinement. Our simulations show that CLBO yields the lowest residual error and requires the fewest evaluations, outperforming Bayesian optimization, simulated annealing, and local search. Mode composition beamforming (MCB) maps confirm that 6-DOF compensation restores spatial resolution and dynamic range to near-ideal levels, even under perturbed reference-source positions. Our experimental validation on a UAV rotor confirms practical feasibility, restoring focal peaks at blade tips. The proposed approach requires no external metrology, it is robust in practice, and it offers an efficient solution to array installation uncertainty. Full article

Millions of persons worldwide experience varying degrees of hearing loss, traditionally addressed through prosthetic solutions such as hearing aids and cochlear implants. However, a significant proportion of individuals cannot benefit from these technologies, cannot access them, or choose not to use them. In this context, non-prosthetic assistive technologies have emerged as a complementary paradigm, leveraging advances in sensing, artificial intelligence, and wearable computing to transform acoustic information into alternative perceptual representations rather than restoring auditory function. This survey provides a review of such systems, focusing on technologies that enhance environmental awareness, communication, and social interaction. Existing approaches are categorized along two main dimensions: the tasks they perform and the platforms on which they operate. Task-oriented analysis includes sound recognition (speech and non-speech), sound source localization, emotion recognition, sign language recognition, and related emerging functionalities. Platform-based analysis emphasizes wearable devices and mobile solutions enabling real-time and context-aware assistance. The survey further highlights key research trends, including real-time auditory scene analysis, portable processing, and artificial intelligence. It shows that recent studies increasingly demonstrate that combining auditory, visual, and haptic modalities improves robustness and usability in real-world conditions, particularly in noisy and dynamic environments. Finally, open challenges such as energy efficiency, latency, evaluation methodologies, and user acceptance are discussed. By synthesizing existing work and identifying open research directions, this survey aims to provide a structured foundation for future developments in intelligent, non-prosthetic assistive systems that redefine how auditory information is accessed and interpreted. 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

With the increasing demand for intelligent fault monitoring, acoustic-based diagnosis has emerged as a promising solution for industrial applications such as pipeline leakage and electrical equipment fault detection. However, complex working conditions and domain shifts significantly degrade model performance, especially when unseen target domain data is unavailable. To address this, we propose an amplitude-phase collaborative augmentation network named AP-CANet tailored for acoustic fault diagnosis. Specifically, the network adaptively aligns amplitude and phase features across multiple source domains and performs label-consistent sample augmentation to enrich data diversity while preserving semantic consistency. A frequency–spatial interaction module further integrates global spectral information with local temporal details to improve feature discriminability. Moreover, we introduce a manifold triplet loss that scales shortest path distances in the feature manifold, encouraging the model to better capture subtle distinctions among hard samples and improving intra-class compactness and inter-class separability. We evaluate the proposed method on two publicly available datasets: the Pipeline Leak Acoustic Dataset (GPLA-12) and the Electrical Sound Dataset (MIMII-DG). Experimental results demonstrate superior performance under domain-shift scenarios, highlighting the method’s potential for scalable and low-cost acoustic fault diagnosis in real-world industrial environments. Full article

Aircraft flyover measurements are used to record the acoustic pressure signals generated by large civil aircraft as they fly over a large-scale microphone array deployed on the ground, thereby obtaining the spatial distribution of aircraft airframe noise and providing technical support for aircraft noise reduction. Aircraft flyover measurements have been widely applied in the research and development of numerous large civil aircraft in Europe and North America since the 1990s. In recent years, aircraft flyover measurements have also been extensively adopted in China, particularly with the rapid development of COMAC C919 large civil aircraft. Computer vision techniques have also been applied to microphone position calibration and aircraft trajectory determination in measurements, which has effectively improved measurement efficiency and accuracy. This paper presents an integrated procedure for aircraft flyover measurements of large civil aircraft in China, including microphone array design, installation, and calibration, noise acquisition system setup and data acquisition, aircraft trajectory determination, and data processing. Full article

Identifying noise sources in exceedance-triggered audio is essential for targeted source tracing and sustainable urban social noise governance. While accurate models require massive labeled data, the acoustic complexity, high redundancy, and imbalanced class distributions of real-world recordings incur prohibitive manual annotation costs, hindering their widespread application in IoT networks. To tackle this bottleneck, we present a label-efficient active learning framework designed to minimize annotation costs by dynamically selecting the most valuable audio samples. Specifically, rather than treating uncertainty, class balance, and diversity as separate query criteria, it encodes uncertainty and dynamic class-aware learning needs into a weighted acoustic feature space, so that diversity-based selection can be performed in a unified manner. Experiments on the UrbanSound8K benchmark and a realistic exceedance-triggered monitoring dataset demonstrate consistent label-efficiency advantages over mainstream methods. Notably, our approach reaches 98% of the fully supervised upper bound on the real-world dataset while reducing the training annotation workload by 85.0% compared to random sampling. On the real-world dataset, the proposed framework yields higher F1-scores for several challenging under-represented categories and reduces the misclassification of dominant sound events relevant to social noise source tracing. Furthermore, cross-site generalization experiments reveal rapid localized adaptation to new monitoring environments, reaching the fully supervised upper bound with only 13% of the target-domain training data. Overall, this study provides a scalable and cost-effective classification framework for urban noise monitoring, offering practical support for noise regulatory authorities and city managers in more targeted noise source tracing and governance. Full article

The problem of performance degradation in current sound source localization algorithms under reverberant and noisy environments remains a critical challenge. Consequently, this paper introduces a novel approach to estimate the 3-D position of sound sources directly from raw audio signals using an artificial neural network (ANN), which improves the performance of sound source localization algorithms under reverberant and noisy environments. Instead of relying on handcrafted features, raw audio signals recorded by a tetrahedral stereo microphone array are fed directly into the ANN. This design eliminates spatial symmetry issues found in 2-D microphone arrays and enhances 3-D localization accuracy. Inspired by human auditory systems, a convolutional layer is added after the input layer to simulate frequency analysis to search localization cues in different frequency bands. Furthermore, the proposed algorithm incorporates residual connections (RC) and squeeze-and-excitation (SE: an attention mechanisms). Residual connections introduce raw features into deeper network layers to prevent localized information loss caused by excessive network depth, while also enabling improved model training stability. The attention mechanism dynamically adjusts weights across and within channels, suppressing interference while enhancing localization-critical cues, thereby playing a pivotal role in boosting the algorithm’s reverberation and noise resistance. Experimental results demonstrate significant improvements: in semi-anechoic chambers, the method reduces localization errors by 0.2 m and increases accuracy by 10%; in conference rooms, errors decrease by 0.26 m with a 21% accuracy gain. These outcomes conclusively validate the effectiveness of the proposed approach in enhancing robustness against reverberation and noise in sound source localization systems. Full article

The soundscape serves as a critical determinant of the quality of urban wetland parks. This study employs a mixed-methods approach to comprehensively evaluate wetland soundscapes. First, field investigations combining sound level measurements and questionnaire surveys were conducted in Aixi Lake Wetland Park to analyze the spatiotemporal characteristics of the soundscape. Second, laboratory-based physiological tracking (using wearable sensors) and cognitive tests (Sustained Attention to Response Task, SART) were utilized to experimentally quantify the restorative benefits of typical soundscapes. The findings reveal that: (1) sound level indicators and sound harmonious degree in urban wetland parks exhibit significant spatiotemporal characteristics and distributional variations; (2) a marked competitive effect among biological, geophysical, and human activity sounds is observed in their spatial distribution; sound harmonious degree demonstrates significant spatial autocorrelation in both global and local models; (3) different sound sources possess varying restorative potentials, with bird song showing the highest restorative effect; the SHDs of biological and geophony, along with L_Aeq, are key factors affecting PRSS; (4) a positive correlation exists between L_Aeq and the PRSS up to 56.4 dB, beyond which PRSS declines with increasing L_Aeq; (5) at the physiological level, short-term exposure to urban wetland park soundscapes can rapidly alleviate stress, with the most pronounced restorative effects occurring within the first 60 s; and (6) in terms of attention, soundscape stimulation reduces SART response times and improves response speed, while bird song from treetops and musical sounds further decrease response errors. Full article

Rapid assessment of ground surface conditions is essential in disaster response and search-and-rescue operations, where drones are increasingly deployed for aerial inspection and victim localization. This paper proposes an active acoustic sensing method for estimating ground surface conditions using a drone-mounted speaker and microphone array. The method is based on the multiple signal classification framework and enables three-dimensional localization of reflection points according to the principle of echolocation. A key feature of the proposed approach is that it shares both hardware and signal processing components with acoustic-based victim search, allowing simultaneous execution of surface sensing and sound source localization (SSL) on a single drone platform without increasing system complexity. Outdoor experiments were conducted to evaluate sensing performance for ground surface anomalies, specifically ground surface depressions and cracks. The experimental results clarify the achievable sensing performance and coverage in real environments and reveal key factors affecting detection performance. The feasibility of simultaneous execution of active acoustic sensing and SSL was also investigated, and the mutual interactions between sensing and localization performance were clarified. These findings highlight both the potential and the practical limitations of integrating environmental sensing and victim localization on a single drone platform. Full article

This study proposes TNAP-DDQN, a deep reinforcement learning method for urban low-altitude UAV path planning under residential noise threshold constraints. With time cost and safety risk as the optimization objectives, operational constraints such as collision risk and maximum AGL altitude are incorporated to achieve coordinated optimization of noise compliance, operational safety, and efficiency. To mitigate action space contraction and training instability induced by multiple constraints, a Noise-Degradation-Mask-based Action Bias Network (NDM-ABN) is introduced at the action selection layer. A three-tier degradation scheme prevents empty candidate sets, while bias-based decision making is applied to approximately tied actions to stabilize the policy. Moreover, multi-step prioritized experience replay (PER) improves sample efficiency and long-horizon return modeling, and potential-based reward shaping (PBRS) transforms sparse constraint signals into auxiliary rewards. Simulation results indicate that: (1) NDM-ABN is the key module for stabilizing the noise-exposure process by suppressing high-noise actions; (2) the required AGL is related to the UAV source noise level and local noise limits, implying the need for differentiated AGL altitude classes; and (3) the maximum admissible UAV source noise level increases as the threshold is relaxed. The proposed method provides quantitative guidance for noise-entry and AGL altitude regulation, while future work will incorporate additional metrics (e.g., A-weighted equivalent sound level) to better capture noise fluctuations and short-term peaks. Full article