With the combination of two critical features from Cummer-Schurig, acoustic cloaking and the scattering cancellation technique, this study reports a hybrid design for the cloaking of elastic cylinders using alternating layered fluids (the effective density to be anisotropic), which are achieved by alternately immersing HGM (a syntactic foam, light solid material with high sound velocity) and Pb (lead, a heavy solid material) in the background fluid medium. The cloaking performance of the proposed design is investigated both by the numerical simulation and by experimental measurement. For a lead cylinder of radius 50 mm, the measured visibility reduction below -5 dB is obtained in the frequency range from 18 kHz to 23 kHz. Compared with the scattering cancellation by the thin elastic shell, the proposed cloaking can be obtained at shorter wavelengths due to the suppression of more higher-order scattering. In addition, the performance of cloaking has no dependence on the incident angles, which has an advantage over the scattering cancellation using scatters distributed unevenly. This is the first experiment using layered fluids to obtain the cloaking of an elastic cylinder, which has potential application in underwater acoustic stealth.

In this paper, an approach for effectively reducing nonlinear distortion in single-backplate condenser microphones is introduced, i.e., most microelectromechanical systems (MEMS) microphones, studio recording condenser microphones, and laboratory measurement microphones. This simple post-processing technique can be easily integrated on external hardware such as an analog circuit, microcontroller, audio codec, digital signal processing unit, or within the Application Specific Integrated Circuit chip in a case of MEMS microphones. It effectively reduces microphone distortion across its frequency and dynamic range, and relies on a single parameter, which can be derived from either the microphone's physical parameters or a straightforward measurement presented in this paper. An optimal estimate of this parameter achieves the best distortion reduction, whereas overestimating it never increases distortion beyond the original level. The technique was tested on a MEMS microphone. The findings indicate that for harmonic excitation, the proposed technique reduces the second harmonic by approximately 40 dB, leading to an effective reduction in the total harmonic distortion. The efficiency of the distortion reduction technique for more complex signals is demonstrated through two-tone and multitone experiments, where second-order intermodulation products are reduced by at least 20 dB.

Sonar remains a major way to detect and discriminate underwater targets by interpreting the echoes. In this study, we used broadband dolphin clicks to detect and classify targets. The peak and notch features of the echo spectra were coded, and echoes were obtained using five-click trains, with the number of clicks changing from 1 to 50. Codes containing the target interpretation were classified by convolutional neural networks (CNNs). Compared to a single click, the increasing number of clicks to 5, 10, 20, and 50 in a train would gradually improve the classification rate of targets by 3%, 6.1%, 8.2%, and 10.5% on average with a signal-to-noise ratio ranging from -10 to 15 dB. The 50-click train outperformed other click trains in target detection and classification. The CNNs achieved an average classification accuracy of 95.2% for a 50-click train, higher than that of the nearest neighbor method by 10.3% across signal-to-noise ratios. Therefore, the usage of dolphin clicks and CNN-based echo encoding technologies constitutes an effective method for enhancing target classification, offering valuable insights for future applications in detecting underwater targets.

Feature-aided tracking integrates supplementary features into traditional methods and improves the accuracy of data association methods that rely solely on kinematic measurements. However, previous applications of feature-aided data association methods in multi-target tracking of passive sonar arrays directly utilized raw features for likelihood calculations, causing performance degradation in complex marine scenarios with low signal-to-noise ratio and close-proximity trajectories. Inspired by the successful application of deep learning, this study proposes BiChannel-SiamDinoNet, an advanced network derived from the Siamese network and integrated into the joint probability data association framework to calculate feature measurement likelihood. This method forms an embedding space through the feature structure of acoustic targets, bringing similar targets closer together. This makes the system more robust to variations, capable of capturing complex relationships between measurements and targets and effectively discriminating discrepancies between them. Additionally, this study refines the network's feature extraction module to address underwater acoustic signals' unique line spectrum and implement the knowledge distillation training method to improve the network's capability to assess consistency between features through local representations. The performance of the proposed method is assessed through simulation analysis and marine experiments.

Road traffic noise is the major component of acoustic environmental pollution both in urban and rural areas. For this reason, much effort has been put into developing models to assess its impact. However, literature models are often suitable for standard conditions but can fail in non-standard ones, i.e., when the single vehicle speed cannot be neglected. Moreover, input data to literature models are not always available, e.g., if the road infrastructure is still in the design phase. The presented approach aims to try to overcome these shortcomings using a microscopic and stochastic-core model, in which the speed of each vehicle can be randomly generated using a specific speed distribution. The validation of the model, investigated through a statistical analysis of simulated continuous equivalent sound pressure levels, the error distribution, and the calculation of commonly used error metrics suggests that the proposed methodology provides good estimations of traffic noise. The errors of the model computed as the differences between measured and simulated sound levels, can be described as a distribution curve with a -0.6 dBA mean and a standard deviation of 2.3 dBA. The error metrics confirm the model's goodness, with a mean absolute error of 1.84 dBA and a coefficient of variation error of 0.03.

To address the issue of tracking highly maneuverable small underwater intruders in the dynamic and heavily interfered environment of harbors, a local sparse motion acoustic flow (LSMAF) computation method constrained by robust high-order flux tensor (RHO-FT) feature has been proposed, along with a LSMAF feature-aided active tracking method. First, potential targets are localized from the dense dynamic clutter background of active sonar echographs using RHO-FT feature maps. Subsequently, on the basis of a dense motion acoustic flow calculation method, a local motion consistency criterion is introduced to the potential target area, establishing a method for calculating LSMAF, which updates the precise motion vectors of potential targets in real time. Finally, all measurements obtained from the RHO-FT feature map are iteratively filtered together with their corresponding motion vectors to maintain the kinematic consistency of the potential target's trajectory. Experiments conducted on a series of cooperative targets in real-world harbor environments show that LSMAF-aided tracking method outperforms the latest developments of tracking for underwater targets in terms of both tracking efficiency and trajectory accuracy.

Directional communication plays a pivotal role in enabling odontocetes to maintain group coordination and social interactions. The fundamental frequency, number of harmonics, and their relative energy distribution in whistles exhibit temporal variation. This study investigated the whistles produced by the Indo-Pacific humpback dolphins (Sousa chinensis) in Xiamen Bay, China. Using computed tomography scanning data, we developed a numerical model of the species and used finite element modeling to examine the beam patterns at both fundamental and harmonic frequencies of whistles, ranging from 3.9 to 64.9 kHz, which corresponds to directivity indices (DIs) between 2.2 and 16.2 dB. We weighted the beams at the fundamental frequencies and harmonics based on their energy distribution to derive composite beam patterns at specific time stamps, allowing us to investigate temporal variations in the corresponding DI within individual whistles. The time-varying properties of DIs were analyzed for various whistle types, including constant, upsweep, downsweep, convex, and sine. Given that harmonics are integer multiples of the fundamental frequency, their contours exhibit similar shapes, whereas the composite DI showed more complexity. These findings indicate that the proportion of energy between the fundamental frequency and harmonics is a key determinant of whistle directivity in Indo-Pacific humpback dolphins.

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.

A surprising number of citations of studies on psychophysical methods come from journals in which the topics do not include psychophysics. Powerful tools have been developed by statisticians to support experimental methods originally borrowed from psychophysics, some of which predate the simplistic and misleading analyses that are commonly cited. This Letter adapts selected results from the mathematical foundations of transformed staircases. Two key results are presented, a more accurate definition of the targeted probability of response and a method to calculate the distribution of signal levels presented-sequentially and asymptotically-that will support advancements in data collection, analysis, and interpretation.

Gransier and Kastelein [J. Acoust. Soc. Am. 155, 396-404 (2024)] present a review of selected studies on temporary threshold shift (TTS) in seals and porpoises. In contrast to the conclusion made in the paper, the results presented are fully consistent with the current understanding that sound exposure level is the best overall predictor of TTSs in marine mammals. If all available TTS studies on seals and porpoises exposed to narrowband noise are included, there is support neither for the conclusion that seals and porpoises are equally susceptible to TTSs nor for their claim that audiograms are poor predictors of the frequency dependence of TTS susceptibility.

A solution method to improve an anechoic chamber at low frequencies with the use of active noise control is presented. The approach uses the Kirchhoff-Helmholtz integral to compute the reflected sound field resulting from the primary sources together with an algorithm to compute the filter coefficients of a controller driving secondary sources on the walls of the enclosure using reference signals as inputs, which are measured on a contour enclosing the primary sources. A causal frequency domain method with conjugate gradient iterations is derived to determine the controller. The method is sufficiently efficient to allow computation of a causal time-domain controller with hundreds of secondary sources and hundreds of reference sensors in two- (2D) or three-dimensional configurations using a fully coupled multiple-input multiple-output system. The paper shows the results of a simulation with 200 secondary sources, 200 reference sensors, and 225 performance sensors based on a 2D finite element simulation. The method is verified in real-time in an experiment with the objective to suppress the reflections from the walls in a smaller 2D setup. Measurements with verification microphones show that the reverberation time is effectively reduced in the real-time experiment.

This paper addresses achieving the high time-bandwidth product necessary for low signal-to-noise ratio (SNR) target detection and localization in complex multipath environments. Time bandwidth product is often limited by dynamic environments and platform maneuvers. This paper introduces data-driven wideband focusing methods for passive sonar that optimize parameterized unitary matrices to align signal subspaces across the frequency band without relying on wave propagation models which are subject to mismatch in complex multipath environments. The methods minimize the log-determinant of the wideband covariance, a measure indicative of matrix rank, ensuring the coherence of wideband data and preserving SNR. We propose two approaches: a fully adaptive method with parameters scaling directly with the number of frequency bins, and a partially adaptive method that shares parameters across frequencies to improve robustness to noise. Simulations are conducted in a shallow-water waveguide scenario to demonstrate the flexibility of data-driven focusing over traditional model-based approaches. Results from the SWellEx-96 S59 event validate our methods, showing improvement in tonal target detection and localization in the presence of strong wideband interference.

The otic capsule and surrounding temporal bone exhibit complex 3D motion influenced by frequency and location of the bone conduction stimulus. The resultant correlation with the intracochlear pressure is not sufficiently understood, thus is the focus of this study, both experimentally and numerically. Experiments were conducted on six temporal bones from three cadaver heads, with BC hearing aid stimulation applied at the mastoid and classical BAHA locations across 0.1-20 kHz. Three-dimensional motions were measured on various skull regions, including the promontory and stapes. Intracochlear pressure was measured using a custom acoustic receiver. The experiment was digitally recreated by a custom finite element model (FEM), based on the LiUHead, with the addition of an auditory periphery. The Young's modulus of the cortical bone domain within the FEM was varied between 4, 8, and 20 GPa. The predicted differential intracochlear pressures aligned with experimental data for most frequencies, and showed that skull deformation, particularly in the otic capsule, depends on skull material properties. Both experimental and FEM results indicated that the otic capsule behaves as a rigid accelerometer, imposing inertial loads on cochlear fluids, even above 7 kHz. Future work should explore the solid-fluid interactions between the otic capsule and cochlear contents.

In their Comment, Tougaard et al. [(2025). J. Acoust. Soc. Am. 157, 538-541] question our conclusion that despite their different audiograms, harbor seals (Phoca vitulina) and harbor porpoises (Phocoena phocoena) have similar susceptibility to temporary hearing threshold shift caused by loud sounds, and claim that our selection of data for analysis was biased. In this Response, we clarify our methods and uphold our original conclusions.

In thermal therapies, accurate estimation of in-tissue power deposition density (PDD) is essential for predicting temperature distributions over time or regularizing temperature imaging. Based on our previous work on ultrasound thermometry, namely, multi-thread thermal strain imaging (MT-TSI), this work develops an in vivo PDD estimation method. Specifically, by combining the TSI model infinitesimal echo strain filter with the bio-heat transfer theory (the Pennes equation), a finite-difference time-domain model is established to allow online extraction of the PDD. An alternating-direction implicit method is adopted to ensure numerical stability and computational efficiency in implementing the model. Based on simulations, the accuracy and effectiveness of the model are examined by comparing a preset PDD distribution with the estimated one. Then, TSI results are obtained from ultrasound data acquired in in vivo experiments; with the PDD estimated from that, TSI distributions are then "predicted" using a validated numerical procedure. The two TSI results are compared to verify the self-consistency of the proposed method. A simplified and more efficient protocol for obtaining an "equivalent spherical PDD" is also discussed.

A complex-valued neural process method, combined with modal depth functions (MDFs) of the ocean waveguide, is proposed to reconstruct the acoustic field. Neural networks are used to describe complex Gaussian processes, modeling the distribution of the acoustic field at different depths. The network parameters are optimized through a meta-learning strategy, preventing overfitting under small sample conditions (sample size equals the number of array elements) and mitigating the slow reconstruction speed of Gaussian processes (GPs), while denoising and interpolating sparsely distributed acoustic field data, generating dense field data for virtual receiver arrays. The predicted field is then integrated with the matched field processing (MFP) method for passive source localization. Validation on the SWellEx-96 waveguide shows significant improvements in localization performance and reduces sidelobes of ambiguity surface compared to traditional MFP and GP-based MFP. Moreover, the proposed kernel based on MDFs outperforms the Gaussian kernel in describing ocean waveguide characteristics. Because of the feature representation of multi-modal mapping, this kernel enhances acoustic field prediction performance and improves the accuracy and robustness of MFP. Simulated and real data are used to verify the validity.

Since traffic flow has not been generated, a traffic noise prediction model based on actual traffic state data cannot be directly applied to the planned road network. Therefore, a regional traffic noise prediction method is proposed to find the upper limit of network noise emission based on design elements. The model is developed with noise predictions of the basic road section, interrupted/continuous intersections, and regional network. Meanwhile, ranges of traffic flow speed and volume are inferred by design elements and constraints between road units are obeyed. A four-scenes experiment to verify the method's accuracy is organized and the average noise difference between the upper limit calculated value and maximum measurement value is 1.53 dBA. All noise differences are positive as the measured noise values may not reach the upper limit of network emission in the experimental state. The method is applied to a network under design elements, and the results show that the model is suitable for the predicting upper limits of noise under design constraints; under the same design elements, noise emission at interrupted intersections is higher than that at continuous intersections. The method can provide a theoretical and data basis for planning network noise protection.

Due to the limited size of the quiet zone created by active headrests (AHR) near the human ear, noise reduction (NR) at the human ear decreases dramatically when the head moves. Combined with a head tracking system can improve the NR performance when the head moves, but most such studies currently only consider head translation. To improve the robustness when the head translates or rotates, an ear-positioning (EP) system based on a depth camera and human pose estimation model is presented in this paper and integrated with AHR. A post processing method is proposed to address extreme scenarios like an ear being occluded. Experimental results show that the EP system can effectively track the movement of ears. The performance of AHR combined with the system is more robust, achieving the lowest 11.7/12.2 dBA (left ear/right ear) NR for white noise in range of 80-2000 Hz when head translates in a 5 × 5 × 2 grid at a 2.5 cm interval and 11.4/13.6 dBA for head rotation within the range of 60° compared to -5.7/-6.9 and -3.7/2.5 dBA without the system.

The identification of vibration and reconstruction of sound fields of plate structures are important for understanding the vibroacoustic characteristics of complex structures. This paper presents a data-physics driven (DPD) model integrated with transfer learning (DPDT) for high-precision identification and reconstruction of vibration and noise radiation of plate structures. The model combines the Kirchhoff-Helmholtz integral equation with convolutional neural networks, leveraging physical information to reduce the need for extensive data. By embedding transfer learning, it enhances generalization across different structures. Two plate models of different sizes and publicly experimental data were used to evaluate the model's performance. Results show that the DPDT model achieves superior prediction accuracy stability, and faster convergence compared to the DPD model, with high R2, normalized cross-correlation, and low normalized mean squared error values, demonstrating its robustness and efficacy in reconstructing sound fields even with limited data points. This approach demonstrates significant potential for practical engineering applications, particularly in bridge vibration and noise control.

Improved hardware and processing techniques such as synthetic aperture sonar have led to imaging sonar with centimeter resolution. However, practical limitations and old systems limit the resolution in modern and legacy datasets. This study proposes using single image super resolution based on a conditioned diffusion model to map between images at different resolutions. This approach focuses on upscaling legacy, low-resolution sonar datasets to enable backward compatibility with newer, high-resolution datasets, thus creating a unified dataset for machine learning applications. The study demonstrates improved performance for classifying upscaled images without increasing the probability of false detection. The increased probability of detection was 7% compared to bicubic interpolation, 6% compared to convolutional neural networks, and 2% compared to generative adversarial networks. The study also proposes two sonar specific evaluation metrics based on acoustic physics and utility to automatic target recognition.

The theory of similitudes provides simple laws by which the response of one system (usually of small size) can be used to predict the response of another system (usually larger). This paper establishes the exact conditions and laws of similitude for the vibrations and acoustic radiation of a panel immersed in a heavy fluid and excited by a turbulent boundary layer. Previous work on vibroacoustic similitude had not considered the problem of a panel radiating in heavy fluid, for which the radiation impedance of the structure must be scaled. The scaling parameters studied here are the dimensions and thickness of the structure, its material properties, the properties of the acoustic domain, and the convective velocity of the turbulent boundary layer. The corresponding scaling laws are derived analytically and verified numerically; in particular, material properties of scaled panels can be determined with simple geometrical constructions on the diagram of Ashby.