Journal of the Acoustical Society of America最新文献

Autistic individuals often exhibit atypical sound perception, yet the specific acoustic properties involved-beyond sound intensity-remain unclear. This study examined whether subcortical processing of harmonicity and frequency differs in autistic children, potentially contributing to altered auditory experiences. Frequency-following responses (FFRs) were recorded from 15 autistic boys (ages 7-14) and nine age-matched neurotypical (NT) boys. In Experiment 1, participants heard three complex tones varying in their degree of harmonicity. No group differences emerged in FFRs to either the envelope or fine structure, suggesting comparable subcortical encoding of harmonicity. In Experiment 2, participants were presented with complex tones differing in the frequency of partials and/or fundamental frequency. Autistic children showed significantly weaker envelope responses across all stimuli and reduced fine structure responses around 500 Hz, while responses at lower and higher frequencies matched NT peers. These findings suggest that atypical subcortical encoding of frequency-specific information may contribute to altered sound perception in autism. Larger-scale studies are needed to confirm these results and connect them to behavioral measures.

The Reflections series takes a look back on historical articles from The Journal of the Acoustical Society of America that have had a significant impact on the science and practice of acoustics.

Acoustic signals with a center frequency of 35 Hz and a full bandwidth of about 4 Hz were transmitted over various ranges along a path extending from north of Svalbard to north of Alaska during the 2019-2020 US-Norwegian Coordinated Arctic Acoustic Thermometry Experiment (CAATEX). Three moorings were installed in the Canada Basin and three in the Nansen Basin, with one mooring in each basin hosting a source. All moorings had vertical receiving arrays, enabling spatial separation of the low-order acoustic normal modes. The modal group delays varied significantly over the year but were roughly consistent with predictions for the decade 2015-2022 based on the World Ocean Atlas 2023. The CAATEX signals traversed nearly the same trans-Arctic acoustic path as the 19.6-Hz signals in the 1994 Transarctic Acoustic Propagation (TAP) experiment. The TAP and CAATEX group delays cannot be directly compared because of the differing carrier frequencies. Thus, an indirect method using the group delays computed using WOA 2023 as a convenient standard was employed, but the large TAP mode-2 travel-time uncertainty precluded definitive comparisons. Nonetheless, CAATEX demonstrated that long-range acoustic transmissions provide precise, year-round measurements of large-scale ocean sound-speed (temperature) variability under the ice.

Ocean-bottom seismometers (OBSs) are used increasingly often to track baleen whale signals, employing single-station ranging techniques such as the three-component (3C) method. By using the orientation of ground motion from OBS components, the 3C method provides robust range estimates of direct-path signals within a validity range that relates to instrument depth. Consequently, the method requires a classification process to determine whether a signal falls within the validity range. Fin whale tracks, composed of 20-Hz notes from six locations, were used to develop and evaluate three classification models: decision trees (DTs), generalized additive models, and neural networks. Models were trained using different data combinations and incorporated a comprehensive set of variables related to channel amplitude, signal quality, polarization, and estimated signal angles. The DT achieved the highest performance, reaching an accuracy of 0.94 on the test data. Key variables for predicting the validity of the 3C ranges included the difference between observed horizontal-to-vertical amplitude ratios and its theoretical value, polarization metrics, and the amplitude of one horizontally oriented OBS component (Y-channel). The resulting framework contributes to improving the utility of seismic data for biological studies, which are critical for marine mammal monitoring and conservation strategies.

The primary goal of this study is to investigate and refine the two-cavity impedance tube method for acoustic characterization of bulk porous materials, specifically addressing previously unexplained inaccuracies in the prediction of surface impedance and absorption coefficients. Unlike the conventional two-thickness approach, the two-cavity method requires only one sample thickness and involves conducting measurements at various air cavity depths behind the sample. The initial analyses revealed previously unidentified numerical instabilities, resulting in anomalous predictions of sound absorption at specific frequencies. Through systematic investigation and use of calculated data, the numerical origins of these anomalies are uncovered and a practical solution, involving the careful selection of cavity depths, is presented. This approach significantly improves predictive accuracy, validating the two-cavity impedance tube method as a robust and effective tool for the acoustic characterization of a wide variety of porous materials, including metallic and nonmetallic open-cell foams and additively manufactured lattice structures.

Spatial audio systems are typically evaluated in comparative listening tests using the same source signal for each condition {such as ABX: ITU-R BS.1116-3 [(2015a) Methods for the Subjective Assessment of Small Impairments in Audio Systems (International Telecommunication Union, Geneva, Switzerland)] and multiple stimulus with hidden reference and anchor ITU-R BS.1534-3 [(2015b) Methods for the Subjective Assessment of Intermediate Quality Level of Audio Systems (International Telecommunication Union, Geneva, Switzerland)]}. However, in augmented reality (AR) scenarios, it is infeasible that the same sound source would exist at the same position in space, both real and virtual; instead, each sound source will emit a different signal. To investigate this discrepancy, a perceptual study is conducted on the effect of source signal similarity when distinguishing different room acoustics conditions. Specifically, these conditions are binaural room impulse responses measured at different distances from the source, modified to all use the same direct sound. Three classes of source signal are investigated in a three-alternative forced choice paradigm: the same speech signal for all conditions, the same speaker but a different sentence for each condition, and a different speaker and a different sentence for each condition. Results show that using different speech recordings significantly reduces the ability to identify differences in room acoustics. This suggests that spatial audio system fidelity requirements could vary depending on the source signals used in the target application; AR audio evaluation should use different signals for comparisons.

The prediction of ocean sound speed fields (SSFs) is critical for underwater communication, marine resource exploration, and environmental monitoring. Due to the powerful generalization ability, deep learning technology has demonstrated its advantages in SSF prediction. However, limited by the processing capabilities of high-dimensional data, current research can only realize the three-dimensional characteristic extraction, without capturing the complete spatiotemporal information of SSF. In this work, we propose the Swin Transformer-UNet model (ST-UNet), which combines the convolutional networks U-Net and Swin Transformer networks, to approach the four-dimensional prediction of SSF. In this model, Swin Transformer network is applied to extract spatiotemporal characteristics through the multi-head self-attention mechanism, while U-Net enhances spatial details via the convolutional feature recovery. The availability and accuracy of the model are demonstrated by the real-life dataset from the South China Sea. It achieves a root mean square error of 0.783 m/s for 24-h SSF prediction based on 7-day historical data, outperforming baseline architectures by 33%-72%.

The design of high-performance receivers with manageable complexity is crucial for underwater acoustic communication, especially in multiple-input multiple-output (MIMO) scenarios. In this paper, a low-complexity MIMO receiver, which is based on variational Bayesian inference (VBI), is proposed. First, an iterative channel estimation model is constructed, which is based on VBI, the high-dimensional channel vector in the MIMO system is decomposed into a parallel connection of multiple low-dimensional channel vectors with different sparsity, and the prior distribution of the low-dimensional channel vectors is jointly modeled using temporal correlation (TC) and sparsity. Next, the low-complexity vector approximate message passing (VAMP) technique is integrated into the VBI framework and a channel estimation method is derived, based on TC-VAMP-VBI. Finally, to reduce the complexity of MIMO channel equalization, a serial iterative equalization algorithm is proposed, which incorporates passive time reversal under the VBI framework. The proposed algorithm was validated using simulations and MIMO communication data collected from two field experiments. The results show that the proposed algorithm can significantly reduce the computational complexity of the MIMO system while maintaining robustness of channel estimation in short data block scenarios.

Accurate and efficient prediction of deep-sea acoustic transmission loss is essential for underwater applications, such as sonar design and underwater communication. In practical acoustic field computation tasks, it is often necessary to analyze the spatial distribution characteristics of the acoustic field in different regions. However, traditional numerical models require large-scale simulations on dense grids to predict acoustic fields over multiple spatial ranges, resulting in high computational cost. To address these limitations, this study proposes a dynamic multi-task U-Net (DMT-UNet) neural network model. Built on a multi-task learning framework, the model can dynamically adjust its network structure for end-to-end joint modeling of acoustic transmission loss at different computation ranges. Incorporating source information and deep-sea sound speed profiles as inputs enhances adaptability to model complex environments. DMT-UNet achieves an average root mean square error of approximately 1.6 dB on simulated deep-sea acoustic field datasets, with computational efficiency improved by more than 98.8% compared to traditional numerical models. Gradient-based visualization reveals how the model reconstructs acoustic field distributions during decoding, demonstrating interpretability. Experimental results show that DMT-UNet yields high prediction accuracy and computational efficiency, while maintaining consistency with physical laws and generalization capability. Thus, DMT-UNet enables real-time, multi-task acoustic modeling in complex deep-sea environments.

Nonreciprocal wave propagation allows for directional energy transport. In this work, wave dynamics is systematically investigated in an elastic lattice that combines nonreciprocal stiffness with viscous damping. After establishing how conventional damping counteracts the system's gain, a non-dissipative form of nonreciprocal damping in the form of gyroscopic damping is introduced. It is found that the coexistence of nonreciprocal stiffness and nonreciprocal damping results in a decoupled control mechanism. The nonreciprocal stiffness is shown to govern the temporal amplification rate, whereas the nonreciprocal damper independently tunes the wave's group velocity and oscillation frequency. This decoupling gives rise to phenomena such as the enhancement of net amplification for slower-propagating waves and also boundary-induced wave interference arising from divergent and convergent reflected wave trajectories with varying growth rates. These findings provide a theoretical framework for designing active metamaterials with more versatile control over their wave propagation characteristics.