Journal of the Acoustical Society of America最新文献

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.

Efficient simulation of object scattering is crucial in virtual and room acoustics, particularly for interactive scenarios where time-varying diffraction must be evaluated repeatedly in real-time. In this context, the rigid sphere is commonly used as a simple geometric representation of more complex three-dimensional objects. Although the analytical solution for rigid sphere scattering is well known, its computation is too involved for interactive audio rendering. In this paper, we propose a physically informed, low-order digital filter approximation of rigid sphere scattering for arbitrary source and receiver positions. For the low-frequency range, the first three terms of the spherical harmonics analytical solution are directly expressed by combined first-order low- and high-pass filters. For high-frequencies, the basic properties of rigid sphere scattering are approximated by modelling the shortest and longest paths reflected or bent around the sphere. Both approximations are combined using a blending function to obtain the wideband result. The magnitude of the low-frequency approximation matches the analytical solution well, yielding mean root mean square errors below 0.5 dB for source and receiver distances greater than twice the sphere radius and about 1.5 dB at smaller distances. For the high-frequency range, the mean error in magnitude is overall larger and is about 2 dB.

An enhanced, model-based framework is presented for estimating the acoustic nonlinearity parameter B/A using the finite amplitude insertion-substitution (FAIS) method in transmission mode. The proposed approach advances harmonic amplitude modeling by eliminating the restrictive plane wave assumption and introducing shape functions that accurately account for transducer diffraction and wave superposition. Additionally, a correction term for second-harmonic attenuation is incorporated, enabling precise characterization across a wide range of attenuation exponents. The framework supports both iterative and direct B/A estimation methods, each validated through comprehensive simulations and experimental measurements. Compared to conventional FAIS methods, this method significantly improves accuracy and robustness in B/A estimation, reduces sensitivity to experimental setup and transducer geometry, and provides practical guidance for optimizing experimental parameters such as sample thickness, distance between source, sample, and receiver, and attenuation measurement.

Sound source localization in complex range-dependent shallow water environments is a challenging problem. The challenges arise from factors such as irregular seabed topography and sound speed profiles, which cannot be quantified by the classical underwater sound propagation models. Although full-wave numerical simulation can accurately simulate the acoustic field propagation in such complex range-dependent environments, applying it directly to the sound source localization problem is difficult because of its high computational cost. To address this problem, this paper proposes an acoustic digital twin model that integrates full-wave numerical simulations in an efficient way to perform sound source localization both accurately and rapidly. Specifically, the spectral element method-based full-wave numerical simulations are used to model the sound propagation in a range-dependent environment for a small number of well-designed representations of sound source locations. The Kriging method is then employed to build a digital twin model based on these numerical results to form a model of the sound field varying continuously with source parameters. Subsequently, as long as the sound field is measured by a hydrophone array, it can be matched with the output acoustic field of the digital twin model based on the matched-field processing principle to realize rapid and accurate source localization in complex underwater environments. Numerical experiments demonstrate that the proposed method offers accurate and low-cost real-time sound source localization in complex marine scenarios.

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.

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.

The calibration of ultrasound diagnostic equipment is essential to ensure their effectiveness and safety. Calibrating acoustic fields using hydrophones involves the measurement of the maximum pressure point. Since the signal at the hydrophone output is proportional to the average pressure incident on its surface, when the active area of the hydrophone is larger than the area of the ultrasonic beam at the focus, the lower acoustic pressures surrounding the point of maximum pressure will cause an underestimation of this value. This phenomenon is referred as the spatial averaging effect. The main limitation in the use of smaller hydrophones is the inherent reduction in sensitivity. The International Electrotechnical Commission standards provide methods to correct for the spatial averaging effect when the ratio of the - 6 dB beam width to the hydrophone diameter (Rbh) is higher than 1.5. In this study, a novel method is presented for spatial averaging correction, developed using computational simulation. It consists of an empirical correction factor and allows extending corrections for Rbh values as low as 0.35, with errors below 3%, addressing the compromise between precision and sensitivity of the hydrophone. This method also generalizes to ultrasonic probes with varying characteristics.

This study evaluates Count-the-Dots Audiogram approaches as a simplified clinically viable method to closely estimate the American National Standards Institute [ANSI (1997). S3.5-1997) Speech Intelligibility Index (SII)] standard in quiet environments. We compared audibility calculations and predicted intelligibility scores between Count-the-Dots methods and multiple ANSI [(1997). S3.5-1997)] SII variants, using eight frequency Band Importance Functions (BIF) for 14 776 audiograms from the National Health and Nutrition Examination Survey dataset. Results showed that Count-the-Dots methods closely approximate the ANSI [(1997). S3.5-1997)] SII model as long as the speech levels and the BIF used for calculations were equivalent between the two methods. This was true for audibility calculations and speech intelligibility predictions. However, deviations occurred at higher speech levels [≥65 dB sound pressure level (SPL)] because of differences in how masking is modeled. Count-the-Dots audiogram approaches offer a clinically viable, intuitive alternative for counseling purposes in quiet settings, particularly at natural speech levels (about 55 dB SPL). However, for speech-in-noise conditions, high-level speech, or aided speech inputs, the ANSI [(1997). S3.5-1997)] SII remains the preferred model because of its more detailed acoustic modeling.

Despite their success in speech processing, neural networks often operate as black boxes, prompting the following questions: What informs their decisions, and how can we interpret them? This work examines this issue in the context of lexical stress. A dataset of English disyllabic words was automatically constructed from read and spontaneous speech. Several convolutional neural network (CNN) architectures were trained to predict stress position from a spectrographic representation of disyllabic words lacking minimal stress pairs (e.g., initial stress WAllet, final stress exTEND), achieving up to 92% accuracy on held-out test data. Layerwise relevance propagation, a technique for neural network interpretability analysis, revealed that predictions for held-out minimal pairs (PROtest vs proTEST) were most strongly influenced by information in stressed versus unstressed syllables, particularly the spectral properties of stressed vowels. However, the classifiers also attended to information throughout the word. A feature-specific relevance analysis is proposed, and its results suggest that the best-performing classifier is strongly influenced by the stressed vowel's first and second formants, with some evidence that its pitch and third formant also contribute. These results reveal deep learning's ability to acquire distributed cues to stress from naturally occurring data, extending traditional phonetic work based around highly controlled stimuli.

Efficient numerical solution of the acoustic Helmholtz equation in heterogeneous media remains challenging, particularly for large-scale problems with spatially varying density-a limitation that restricts applications in biomedical acoustics and seismic imaging. A fast iterative solver that extends the convergent Born series [Osnabrugge, Leedumrongwatthanakun, and Vellekoop, J. Comput. Phys. 322, 113-124 (2016)] method to handle arbitrary variations in sound speed, density, and absorption simultaneously is presented. This approach reformulates the Helmholtz equation as a first-order system and applies the universal split-preconditioner from Vettenburg and Vellekoop [arXiv:2207.14222v2 (2022)], yielding a matrix-free algorithm that leverages Fast Fourier Transforms for computational efficiency. Unlike existing Born series methods, this solver accommodates heterogeneous density without requiring expensive matrix decompositions or preprocessing steps, making it suitable for large-scale three-dimensional problems with minimal memory overhead. The method provides forward and adjoint solutions, enabling its application for inverse problems. Accuracy is validated through comparison against an analytical solution and the solver's practical utility is demonstrated through transcranial ultrasound simulations. The solver achieves convergence for strong scattering scenarios, offering a computationally efficient alternative to time-domain methods and matrix-based Helmholtz solvers for applications ranging from medical ultrasound treatment planning to seismic exploration.