Journal of the Acoustical Society of America最新文献

Speech takes place in physical environments with visual and acoustic properties, yet how these elements and their interaction influence speech production is not fully understood. While a room's appearance can suggest its acoustics, it is unclear whether people adjust their speech based on this visual information. Previous research shows that higher reverberation leads to reduced speech level, but how auditory and visual information interact in this process remains limited. This study examined how audiovisual information affects speech level by immersing participants in virtual environments with varying reverberation and room visuals (hemi-anechoic room, classroom, and gymnasium) while completing speech tasks. Speech level was analyzed using generalized additive mixed-effects modeling to assess temporal changes during utterances across conditions. Results showed that visual information significantly influenced speech level, though not strictly in line with expected acoustics or perceived room size; auditory information had a stronger overall effect than visual information. Visual information had an earlier influence that diminished over time, whereas the auditory effect increased and plateaued. These findings contribute to the understanding of multisensory integration in speech control and have implications in enhancing vocal performance and supporting more naturalistic communication in virtual environments.

Detection, classification, and localisation of animal sounds are essential in many ecological studies, including density estimation and behavioural studies. Real-time acoustic processing can also be used in mitigation exercises, with the possibility of curtailing harmful human activities when animals are present. Animal vocalisations vary widely, and there is no single detection algorithm that can robustly detect all sound types. Human-in-the loop analysis is often required to validate algorithm performance and deal with unexpected noise sources such as are often encountered in real-world situations. The PAMGuard software combines advanced automatic analysis algorithms, including AI methods, with interactive visual tools allowing users to develop efficient workflows for both real-time use and for processing archived datasets. A modular framework enables users to configure multiple detectors, classifiers, and localisers suitable for the equipment and species of interest in a particular application. Multiple detectors for different sound types can be run concurrently on the same data. An extensible "plug-in" interface also makes it possible for third parties to independently develop new modules to run within the software framework. Here, we describe the software's core functionality, illustrated using workflows for both real-time and offline use, and present an update on the latest features.

Reliable acoustic path (RAP) channels support stable long-range propagation, but deep-sea communication over RAP is constrained by strong multipath. South China Sea measurements reveal stable arrival structures across multiple source depths, with distinct arrival branches and reverberation tails from scattering at interface inhomogeneities. A RAP-adaptive time-reversal equalizer suppresses multipath by reconstructing the channel impulse response via physics-guided statistical fitting, modeling it as a superposition of discrete multipaths and reverberation. Performance is evaluated using frequency-hopping spread spectrum M-ary frequency-shift keying and direct-sequence spread spectrum M-ary phase-shift keying and quantified with a network-level throughput analysis. Experiments demonstrate reduced inter-symbol interference and improved link reliability, indicating RAP-adaptive time-reversal equalizer as a practical physical-layer method for deep-sea acoustic networks.

Blunt force trauma to the larynx can cause significant damage, resulting in displaced laryngeal cartilage fractures. Vertical misalignment of the left or right vocal fold (VF) in the inferior-superior direction and scarring of the VF tissue are common outcomes. The influence of inferior-superior VF displacement and VF scarring on phonation was investigated using synthetic, self-oscillating VF models in a physiologically-representative facility. Acoustic, kinematic, and aerodynamic parameters were assessed as a function of inferior-superior vertical displacement and asymmetric VF stiffness. The combination of vertical misalignment and asymmetric VF tissue stiffness became most prominent when the inferior-superior misalignment of the VFs exceeded the thickness of the medial surface. Only a small degree of stiffness asymmetry was tolerated before VF kinematics and acoustics were significantly degraded. The position of the scarred VF relative to the healthy one also influenced outcomes. If the stiffer VF was positioned inferior to the normal VF, phonatory outcomes were poorer than when it was positioned superior to the normal VF. Measures of shimmer and jitter were more than twice as high, while cepstral peak prominence was 3-5 dB lower.

Passive acoustic monitoring (PAM) techniques have shown great potential in studying underwater gas plumes by leveraging bubble resonance signals. Traditional bubble detection methods generally operate on the mixture of bubble and ambient sounds, which cannot achieve satisfactory performance in low SNR environments. In this study, we propose a deep learning (DL) based bubble sound separation method to extract the bubble waveform from the noisy mixture prior to detection, thereby enhancing the bubble detection performance. To obtain the labeled training data, we developed a numerical simulation framework based on bubble acoustic theories to generate the ground truth bubble sounds, which are then mixed with diverse noises. Experiments were conducted with both simulated data and realistic PAM recordings. The simulation experiments under different noise conditions demonstrate that the DL models can effectively extract bubble sound, even when their features are barely visible in the time-frequency domain. In the real-world experiment, the trained model was applied to the PAM recordings collected in Haima cold seep, and we found a negative correlation between bubble release rate and ambient pressure when the hydrophones were near the gas plumes, which is in accordance with existing literature and further validates the proposed method's effectiveness.

Acoustic lens focusing is a commonly used method in high-intensity focused ultrasound (HIFU). However, traditional acoustic lens focusing suffers from low focusing efficiency and excessive sidelobes, which affect the efficacy and safety of HIFU treatment. To address this issue, this paper designs a periodic trapezoidal‑groove acoustic metasurface lens by leveraging the extraordinary acoustic transmission effect. Subsequently, its focal sound‑pressure level is calculated through theoretical analysis and finite‑element simulation, and is further validated experimentally. Finally, the influence of structural parameters-such as the period, center width, depth, and taper angle of the trapezoidal groove, as well as the amplitude of the excitation source-on the focusing performance of the acoustic metasurface lens is systematically analyzed. The results demonstrate that the periodic trapezoidal‑groove acoustic metasurface lens can further enhance focusing and suppress sidelobes within a specific frequency range; the frequency corresponding to the maximum sound pressure is determined by the period of the trapezoidal groove; and the shift of Wood's anomaly frequency is primarily governed by the groove depth. This study provides insights for the development of high‑performance acoustic‑lens-focused ultrasound transducers.

Otoacoustic emissions represent cochlear responses to auditory stimuli, enabling the investigation of air conduction (AC) and bone conduction (BC) transmission. This study developed and validated a non-invasive, objective method for measuring the sensitivity difference between AC and BC transmission, here termed bone-air difference transfer property (BADTP), using stimulus frequency otoacoustic emission (SFOAE). The BADTP was defined as the difference between the AC transfer property and the BC transfer property. To cross-validate the objective approach, BADTP was compared with subjectively obtained hearing thresholds. Measurements were conducted across frequencies from 1000 to 4000 Hz in ten individuals with normal hearing. Results revealed that the mean differences between the two methods were within 2 dB at frequencies from 1000 to 1600 Hz, while both methods showed similar trends from 1850 to 4000 Hz. The proposed SFOAE-based method for measuring provides valuable insight into BC transmission, with potential applications for objective assessment of BC function in research settings.

The acoustic reflection coefficient of a layered seabed exhibits an oscillatory structure that varies with frequency and grazing angle. These oscillatory characteristics are linked to the seabed's stratification and its geoacoustic parameters. Through numerical simulation, the influence of seabed layering and geoacoustic parameters on the frequency-angle oscillatory structure of the bottom reflection coefficient (BRC) is investigated. Based on this, a deep learning geoacoustic inversion method is introduced for retrieving near-field seabed layering and its geoacoustic parameters from the frequency- and angle-dependent bottom reflection coefficient. A deep neural network model based on self-attention and cross-attention mechanism, Cross-ViT, is employed to learn features from the two-dimensional BRC matrix. The model is trained to perform the inversion of geoacoustic parameters using a multi-task learning strategy with gradient normalization (GradNorm). Simulation results indicate that, compared to convolutional neural network and transformer models, the presented model more effectively learns the mapping between seabed reflection characteristics and multiple geoacoustic parameters and possesses relatively strong noise robustness. The method's effectiveness is validated using near-field acoustic data from an acoustic inversion experiment conducted on the northern continental shelf of the South China Sea in 2022.

The ability to infer a general representation of the acoustic environment from a reverberant recording is a key objective in numerous applications. We propose a multi-stage approach that integrates task-agnostic representation learning with uncertainty quantification. Leveraging the conformal prediction framework, our method models the error incurred in the estimation of the acoustic environment embedded in a reverberant recording, which reflects the ambiguity inherent in distinguishing between an unknown source signal and the induced reverberation. Although our approach is flexible and agnostic to specific downstream objectives, experiments on real-world data demonstrate competitive performance on established parameter estimation tasks when compared to baselines trained end-to-end or with contrastive losses. Furthermore, a latent disentanglement analysis reveals the interpretability of the learned representations, which effectively capture distinct factors of variation within the acoustic environment.

To address the non-degradability and toxicity of conventional acoustic materials, this study proposes a sustainable spiral-shaped sound absorber composed of plant fiber-based fibrous paper and recycled coffee waste (CW). The strong mechanical bonding between CW and Kozo fibrous paper in this composite acoustic material was observed using metallurgical microscopy, resulting in an environmentally friendly structure capable of controlling broadband noise. A prediction model based on parallel-slit theory was developed to evaluate the influence of key structural parameters-CW layer mass density, fibrous paper length, and absorber width-on sound absorption coefficients. Optimization reveals that wide spiral-shaped geometry paired with a high-density CW layer (0.04-0.05 kg/m2) enhances low-frequency noise reduction (<1000 Hz), whereas narrow configurations with a medium-density CW layer (0.03-0.04 kg/m2) improves high-frequency attenuation (>2000 Hz). The sound absorption coefficients of five prepared samples were measured using the two-microphone impedance tube method. The sound absorption coefficient showed significant improvement with the addition of an appropriate amount of CW in the mid- and high-frequency range. This work advances the development of lightweight, efficient, and sustainable acoustic solutions, providing a scalable strategy for the next generation of eco-friendly materials in line with circular economy principles and low-carbon manufacturing practices.