The 50 most-cited articles in The Journal of the Acoustical Society of America, 2024

A method is presented for estimating and reconstructing the sound field within a room using physics-informed neural networks. By incorporating a limited set of experimental room impulse responses as training data, this approach combines neural network processing capabilities with the underlying physics of sound propagation, as articulated by the wave equation. The network's ability to estimate particle velocity and intensity, in addition to sound pressure, demonstrates its capacity to represent the flow of acoustic energy and completely characterise the sound field with only a few measurements. Additionally, an investigation into the potential of this network as a tool for improving acoustic simulations is conducted. This is due to its proficiency in offering grid-free sound field mappings with minimal inference time. Furthermore, a study is carried out which encompasses comparative analyses against current approaches for sound field reconstruction. Specifically, the proposed approach is evaluated against both data-driven techniques and elementary wave-based regression methods. The…

Ocean sound pressure field prediction, based on partially measured pressure magnitudes at different range-depths, is presented. Our proposed machine learning strategy employs a trained neural network with range-depth as input and outputs complex acoustic pressure at the location. We utilize a physics-informed neural network (PINN), fitting sampled data while considering the additional information provided by the partial differential equation (PDE) governing the ocean sound pressure field. In vast ocean environments with kilometer-scale ranges, pressure fields exhibit rapidly fluctuating phases, even at frequencies below 100 Hz, posing a challenge for neural networks to converge to accurate solutions. To address this, we utilize the envelope function from the parabolic-equation technique, fundamental in ocean sound propagation modeling. The envelope function shows slower variations across ranges, enabling PINNs to predict sound pressure in an ocean waveguide more effectively. Additional PDE information allows PINNs to capture PDE solutions even with a limited amount of training data,…

This experimental study investigates the effect of blade phase angle on noise attenuation in two adjacent, electronically synchronized propellers. Acoustic measurements were performed in an aeroacoustic wind tunnel with a distributed electric propulsion system that involved the adjustment of relative phase angles of 2-bladed propellers between Δψ = 0° and 90°. Ranges of advance ratios (J = 0-0.73) were investigated at a fixed propeller rotation speed of 5000 rpm. The investigation explored the impact on noise directivity and frequency characteristics. The findings reveal significant reductions in noise directivity and tonal noise at the blade pass frequency (BPF). A relative phase angle of Δψ = 90° demonstrated the maximum noise reduction, with an 8 dB decrease at the first BPF and a 2 dB reduction in overall sound pressure level at J = 0. For in-flow conditions (J > 0), a relative phase angle of Δψ = 90° resulted in significant noise reductions of about 24 dB in the first BPF and 6 dB in overall sound pressure level, compared to Δψ = 0°. These observations offer critical insights…

Accurate broadband modeling of underwater acoustic channels is vital for underwater acoustic detection, localization, and communication. Conventional modeling methodologies, based on methods such as the finite element method, finite difference method, and boundary element method, generally facilitate computation for only a single frequency at a time. However, in broadband modeling, this characteristic presents limitations, requiring multiple computations across frequencies, thereby leading to significant time challenges. To solve this problem, we propose a rapid broadband modeling approach using physics-informed neural networks. By integrating the modal equation of normal modes as a regularization term within the neural network's loss function, the method can achieve rapid broadband modeling of underwater acoustic channel with a sparse set of frequency sampling points. Operating in range-independent underwater environments with a liquid semi-infinite seabed, the method proficiently predicts the channel response across the frequency band from 100 to 300 Hz. Compared to the results…

The waveguide acoustic black hole (WAB) effect is a promising approach for controlling wave propagation in various applications, especially for attenuating sound waves. While the wave-focusing effect of structural acoustic black holes has found widespread applications, the classical ribbed design of waveguide acoustic black holes (WABs) acts more as a resonance absorber than a true wave-focusing device. In this study, we employ a computational design optimization approach to achieve a conceptual design of a WAB with enhanced wave-focusing properties. We investigate the influence of viscothermal boundary losses on the optimization process by formulating two distinct cases: one neglecting viscothermal losses and the other incorporating these losses using a recently developed material distribution topology optimization technique. We compare the performance of optimized designs in these two cases with that of the classical ribbed design. Simulations using linearized compressible Navier-Stokes equations are conducted to evaluate the wave-focusing performance of these different designs.…

Practical acoustic propagation modeling is significantly affected by ocean dynamics, and then can be exploited in numerous oceanic applications, where "practical" refers to modeling acoustic propagation in simulations that mimic real-world ocean environments. Physics-based numerical models provide approximate solutions of wave equation and rely on accurate prior environmental knowledge while the environment of practical scenarios is largely unknown. In contrast, data-driven machine learning offers a promising avenue to estimate practical acoustic propagation by learning from dataset. However, collecting such a high-quality, noise-free, and dense dataset remains challenging. Under the practical hurdle posed by the above approaches, the emergence of physics-informed neural network (PINN) presents an alternative to integrate physics and data but with limited representation capacity. In this work, a framework, termed spatial domain decomposition-based physics-informed neural networks (SPINNs), is proposed to enhance the representation capacity in spatially dependent oceanic scenarios…

Noise from unmanned aerial vehicles, commonly referred to as "drones," will likely shape our acoustic environment in the near future. Yet, reactions of the population to this new noise source are still little explored. The objective of this study was to investigate short-term noise annoyance reactions to drones in a controlled laboratory experiment. Annoyance to (i) two quadcopters of different sizes in relation to common contemporary transportation noise sources (jet aircraft, propeller aircraft, helicopters, single car passbys), and (ii) different drone maneuvers (takeoff; landing; high, medium, and low flybys) flown at different speeds and elevations was systematically assessed. The results revealed that, at the same sound exposure level, drones are perceived as substantially more annoying than other airborne vehicles and passenger cars. Furthermore, for drone maneuvers, landings, and takeoffs are more annoying than flybys, as are maneuvers flown at low speed. Different loudness metrics (LAE, LDE, effective perceived noise level, psychoacoustic loudness level) accounted for drone…

Piezoelectric micro-electro-mechanical-system (MEMS) speakers are emerging as promising implementations of loudspeakers at the microscale, as they are able to meet the ever-increasing requirements for modern audio devices to become smaller, lighter, and integrable into digital systems. In this work, we propose a finite element model (FEM)-assisted lumped-parameters equivalent circuit for a fast and accurate modeling of these types of devices. The electro-mechanical parameters are derived from a pre-stressed FEM eigenfrequency analysis, to account for arbitrarily complex geometries and for the shift of the speaker resonance frequency due to an initial non-null pre-deflected configuration. The parameters of the acoustical circuit are instead computed through analytical formulas. The acoustic short-circuit between the speaker front and rear sides is taken into account through a proper air-gaps modeling. The very good matching in terms of radiated sound pressure level among the equivalent circuit predictions, FEM simulations, and experimental data proves the ability of the proposed…

The channel vocoder has become a useful tool to understand the impact of specific forms of auditory degradation-particularly the spectral and temporal degradation that reflect cochlear-implant processing. Vocoders have many parameters that allow researchers to answer questions about cochlear-implant processing in ways that overcome some logistical complications of controlling for factors in individual cochlear implant users. However, there is such a large variety in the implementation of vocoders that the term "vocoder" is not specific enough to describe the signal processing used in these experiments. Misunderstanding vocoder parameters can result in experimental confounds or unexpected stimulus distortions. This paper highlights the signal processing parameters that should be specified when describing vocoder construction. The paper also provides guidance on how to determine vocoder parameters within perception experiments, given the experimenter's goals and research questions, to avoid common signal processing mistakes. Throughout, we will assume that experimenters are interested…

Sound field reconstruction (SFR) augments the information of a sound field captured by a microphone array. Using basis function decomposition, conventional SFR methods are straightforward and computationally efficient but may require more microphones than needed to measure the sound field. Recent studies show that pure data-driven and learning-based methods are promising in some SFR tasks, but they are usually computationally heavy and may fail to reconstruct a physically valid sound field. This paper proposes a compact acoustics-informed neural network (AINN) method for SFR, whereby the Helmholtz equation is exploited to regularize the neural network. As opposed to pure data-driven approaches that solely rely on measured sound pressures, the integration of the Helmholtz equation improves robustness of the neural network against variations during the measurement processes and prompts the generation of physically valid reconstructions. The AINN is designed to be compact and able to predict not only the sound pressures but also sound pressure gradients within a spatial region of…

Aquatic animals (invertebrates, fishes, and marine mammals) are encountering an increasing array of underwater anthropogenic noises that can disrupt and even harm ecosystems as well as the lives of individuals and populations. Sources of anthropogenic noise include, but are not limited to, shipping, offshore exploration and production for fossil fuels, and the construction and operation of wind farms. Because of the continuing increase in anthropogenic noise, research on its potential effects on aquatic animals has intensified over the past two decades. However, a major under-explored issue is that noise is only one type of anthropogenic pressure acting (often simultaneously) on animals. Indeed, multiple simultaneous anthropogenic pressures are likely to affect how aquatic animals respond to each of the individual stimuli. Moreover, animal responses may be very different in the presence of multiple pressures as compared to when there is only a single anthropogenic source. We suggest refocusing of aquatic noise so that research prioritizes studies that deal with the interaction of…

A physics-informed neural network (PINN) enables the estimation of horizontal modal wavenumbers using ocean pressure data measured at multiple ranges. Mode representations for the ocean acoustic pressure field are derived from the Hankel transform relationship between the depth-dependent Green's function in the horizontal wavenumber domain and the field in the range domain. We obtain wavenumbers by transforming the range samples to the wavenumber domain, and maintaining range coherence of the data is crucial for accurate wavenumber estimation. In the ocean environment, the sensitivity of phase variations in range often leads to degradation in range coherence. To address this, we propose using OceanPINN [Yoon, Park, Gerstoft, and Seong, J. Acoust. Soc. Am. 155(3), 2037-2049 (2024)] to manage spatially non-coherent data. OceanPINN is trained using the magnitude of the data and predicts phase-refined data. Modal wavenumber estimation methods are then applied to this refined data, where the enhanced range coherence results in improved accuracy. Additionally, sparse Bayesian learning,…

This experimental study investigated whether infants use iconicity in speech and gesture cues to interpret word meanings. Specifically, we tested infants' sensitivity to size sound symbolism and iconic gesture cues and asked whether combining these cues in a multimodal fashion would enhance infants' sensitivity in a superadditive manner. Thirty-six 14-17-month-old infants participated in a preferential looking task in which they heard a spoken nonword (e.g., "zudzud") while observing a small and large object (e.g., a small and large square). All infants were presented with an iconic cue for object size (small or large) (1) in the pitch of the spoken non-word (high vs low), (2) in gesture (small or large), or (3) congruently in pitch and gesture (e.g., a high pitch and small gesture indicating a small square). Infants did not show a preference for congruently sized objects in any iconic cue condition. Bayes factor analyses showed moderate to strong support for the null hypotheses. In conclusion, 14-17-month-old infants did not use iconic pitch cues, iconic gesture cues, or iconic…

Sound absorption below 1000 Hz has been extremely difficult through traditional barriers and absorbers, but it is required for noise control of appliances and machineries. Existing passive acoustic metamaterials attenuate low-frequency noise but with narrow bandwidths and bulky sizes. Hence, this paper proposes an acoustic metamaterial with enclosed symmetrical labyrinthine air channels and two micro-slits (configuration 1, identical slits; configuration 2, unequal length slits) at the end channels. Its theoretical model is established by acoustic impedance analysis using electro-acoustic analogy and validated numerically and experimentally. Sound absorption is found to happen as a result of impedance matching, Fabry-Perot-like labyrinthine resonances, and thermo-viscous losses in micro-slits. Parametric investigations reveal that increase in the number of channels, channel length, total height, and outer panel thickness shifts sound absorption peak to lower frequency but also decreases the magnitude and frequency range of absorption. Decreasing the channel width and slit width…

For the sonar arrays mounted on an unmanned underwater vehicle (UUV), the direction-of-arrival (DOA) estimation of the far-field (FF) weak sources is influenced by the near-field (NF) interferences generated from the radiated self-noise of the UUV and the FF interferences simultaneously. To address the problem, a sparsity-based DOA estimation method resistant to the NF and FF interferences is proposed in this paper. This method isolates the FF signals from the NF signals by sparse reconstruction. Additionally, subspace projection is applied to address the masking problem of the weak target signal by the strong interferences in the spatial domain, effectively enhancing the capacity of estimating the DOA of the weak target signal in the presence of strong interferences. Numerical simulations and experimental results demonstrate the effectiveness of the proposed method. Compared to other advanced DOA estimation methods, the proposed method exhibits better DOA estimation performance in the presence of strong NF and FF interferences.

Metrics to be used in noise impact assessment must integrate the physical acoustic characteristics of the sound field with relevant biology of animals. Several metrics have been established to determine and regulate underwater noise exposure to aquatic fauna. However, recent advances in understanding cause-effect relationships indicate that additional metrics are needed to fully describe and quantify the impact of sound fields on aquatic fauna. Existing regulations have primarily focused on marine mammals and are based on the dichotomy of sound types as being either impulsive or non-impulsive. This classification of sound types, however, is overly simplistic and insufficient for adequate impact assessments of sound on animals. It is recommended that the definition of impulsiveness be refined by incorporating kurtosis as an additional parameter and applying an appropriate conversion factor. Auditory frequency weighting functions, which scale the importance of particular sound frequencies to account for an animal's sensitivity to those frequencies, should be applied. Minimum phase…

Underwater acoustic target recognition based on passive sonar faces numerous challenges in practical maritime applications. One of the main challenges lies in the susceptibility of signal characteristics to diverse environmental conditions and data acquisition configurations, which can lead to instability in recognition systems. While significant efforts have been dedicated to addressing these influential factors in other domains of underwater acoustics, they are often neglected in the field of underwater acoustic target recognition. To overcome this limitation, this study designs auxiliary tasks that model influential factors (e.g., source range, water column depth, or wind speed) based on available annotations and adopts a multi-task framework to connect these factors to the recognition task. Furthermore, we integrate an adversarial learning mechanism into the multi-task framework to prompt the model to extract representations that are robust against influential factors. Through extensive experiments and analyses on the ShipsEar dataset, our proposed adversarial multi-task model…

The soundscape approach has been utilised in recent research for residential environments, but applications to indoor working environments remain limited. This study investigated the suitability of affective quality evaluations of open-plan office acoustics. Perceptual assessments were completed by occupants of eight open-plan office floorplates, using affective dimensions outlined in PD ISO/TS 12913-3:2019 and indoor soundscape studies. Participant demographic, contextual, work-related quality, and psychological well-being data were collected. Workstation noise levels were measured following BS ISO 22955:2021. Principal component analysis identified Pleasantness, Eventfulness, and Emptiness as the main perceptual dimensions, cumulatively explaining 56% of the total variance. Results indicate the suitability of the PD ISO/TS 12913-3:2019 two-dimensional model for open-plan offices. The perceived presence of human sounds negatively correlated with ISO Pleasantness yet positively correlated with ISO Eventfulness. Participant gender and aural diversity mediated ISO Pleasantness.…

Underwater acoustic target recognition has emerged as a prominent research area within the field of underwater acoustics. However, the current availability of authentic underwater acoustic signal recordings remains limited, which hinders data-driven acoustic recognition models from learning robust patterns of targets from a limited set of intricate underwater signals, thereby compromising their stability in practical applications. To overcome these limitations, this study proposes a recognition framework called M3 (multitask, multi-gate, multi-expert) to enhance the model's ability to capture robust patterns by making it aware of the inherent properties of targets. In this framework, an auxiliary task that focuses on target properties, such as estimating target size, is designed. The auxiliary task then shares parameters with the recognition task to realize multitask learning. This paradigm allows the model to concentrate on shared information across tasks and identify robust patterns of targets in a regularized manner, thus, enhancing the model's generalization ability. Moreover,…

Geoacoustic inversion can be a computationally expensive task in high-dimensional parameter spaces, typically requiring thousands of forward model evaluations to estimate the geoacoustic environment. We demonstrate Bayesian optimization (BO), an efficient global optimization method capable of estimating geoacoustic parameters in seven-dimensional space within 100 evaluations instead of thousands. BO iteratively searches parameter space for the global optimum of an objective function, defined in this study as the Bartlett power. Each step consists of fitting a Gaussian process surrogate model to observed data and then choosing a new point to evaluate using a heuristic acquisition function. The ideal acquisition function balances exploration of the parameter space in regions with high uncertainty with exploitation of high-performing regions. Three acquisition functions are evaluated: upper confidence bound, expected improvement (EI), and logarithmically transformed EI. BO is demonstrated for both simulated and experimental data from a shallow-water environment and rapidly estimates…

This paper presents the results of a listening experiment designed to assess annoyance and perceived loudness (PL) for several unmanned aircraft system (UAS) operations, with the listener simulated in indoor and outdoor positions. This research investigated (i) how participant responses change depending on UAS operation, (ii) which broadband metrics are most suitable for representing annoyance and PL, (iii) differences in noise level required to result in equal participant responses to different operations, and (iv) which sound quality metrics (SQMs) are significant for UAS noise perception. Results indicate annoyance and PL responses were greatest for landing operations with flyovers being the least annoying or loud. LAeq, LASmax, and loudness (N5) were the strongest predictors in representing annoyance. Offset analysis predicted small differences in annoyance responses between flyovers and other operations, but also indicated that flyovers would require an increase to LASmax of 3.3 to 6.3 dB compared to other operations to achieve equal PL. Loudness was the most significant SQM,…

The soundscape approach provides a basis for considering the holistic perception of sound environments in context. Whereas steady advancements have been made in methods for assessment and analysis, a gap exists for comparing soundscapes and quantifying improvements in the multidimensional perception of a soundscape. To this end, there is a need for the creation of single value indices to compare soundscape quality which incorporate context, aural diversity, and specific design goals for a given application. Just as a variety of decibel-based indices have been developed for various purposes (e.g., LAeq, LCeq, L90, Lden, etc.), the soundscape approach requires the ability to create original indices for different uses, which share a common language and understanding. Therefore, a unified framework for creating bespoke and reference single index measures of soundscape perception is proposed, allowing for different metrics to be defined in the future. This framework is based on a four-step test-target paradigm wherein a desired soundscape perception is defined as a target distribution…

Typological research shows that across languages, trilled [r] sounds are more common in adjectives describing rough as opposed to smooth surfaces. In this study, this lexical research is built on with an experiment with speakers of 28 different languages from 12 different families. Participants were presented with images of a jagged and a straight line and imagined running their finger along each. They were then played an alveolar trill [r] and an alveolar approximant [l] and matched each sound to one of the lines. Participants showed a strong tendency to match [r] with the jagged line and [l] with the straight line, even more consistently than in a comparable cross-cultural investigation of the bouba/kiki effect. The pattern is strongest for matching [r] to the jagged line, but also very strong for matching [l] to the straight line. While this effect was found with speakers of languages with different phonetic realizations of the rhotic sound, it was weaker when trilled [r] was the primary variant. This suggests that when a sound is used phonologically to make systemic meaning…

Both the biological effects and acoustic emissions generated by cavitation are functions of bubble dynamics. Monitoring of acoustic emissions is therefore desirable to improve treatment safety and efficacy. The relationship between the emission spectra and bubble dynamics is, however, complex. The aim of this study was to characterise this relationship for single microbubbles using simultaneous ultra-high-speed optical imaging and passive acoustic mapping of cavitation emissions. As expected, both the number of discrete harmonics and broadband content in the emissions increased with increasing amplitude of bubble oscillation, but the spectral content was also dependent upon other variables, including the frequency of bubble collapse and receiving transducer characteristics. Moreover, phenomena, such as fragmentation and microjetting, could not be distinguished from spherical oscillations when using the full duration acoustic waveform to calculate the emission spectra. There was also no correlation between the detection of broadband noise and widely used thresholds for distinguishing…

Speech sounds exist in a complex acoustic-phonetic space, and listeners vary in the extent to which they are sensitive to variability within the speech sound category ("gradience") and the degree to which they show stable, consistent responses to phonetic stimuli. Here, we investigate the hypothesis that individual differences in the perception of the sound categories of one's language may aid speech-in-noise performance across the adult lifespan. Declines in speech-in-noise performance are well documented in healthy aging, and are, unsurprisingly, associated with differences in hearing ability. Nonetheless, hearing status and age are incomplete predictors of speech-in-noise performance, and long-standing research suggests that this ability draws on more complex cognitive and perceptual factors. In this study, a group of adults ranging in age from 18 to 67 years performed online assessments designed to measure phonetic category sensitivity, questionnaires querying recent noise exposure history and demographic factors, and crucially, a test of speech-in-noise perception. Results show…

Electric drones serve diverse functions, including delivery and surveillance. Nonetheless, they encounter significant challenges due to their annoying noise emissions. To address this issue, a sound database was created from experiments conducted in a hover-test-bench and real flights operated indoors. These experiments involved a wide range of parameter variations and operational conditions. A global digital user study involving 578 participants was conducted to assess drone noise annoyance. Furthermore, correlations between annoyance levels, psychoacoustic metrics, sociocultural factors, and technical/operational parameters were analyzed. The effects of implementing acoustic optimization modifications on the drone's performance were quantified with a conceptual design tool. The findings indicate that reducing the levels of loudness, sharpness, tonality, and roughness or fluctuation strength led to an improvement in annoyance. Differences in variable importance of psychoacoustic metrics dependent on the specific model were found. Sociocultural factors did not affect annoyance.…

The dynamic (acoustic pressure) and kinematic (acoustic acceleration and velocity) properties of time-limited signals are studied in terms of acoustic dose metrics as might be used to assess the impact of underwater noise on marine life. The work is relevant for the study of anthropogenic transient acoustic signals, such as airguns, pile driving, and underwater explosive sources, as well as more generic transient signals from sonar systems. Dose metrics are first derived from numerical simulations of sound propagation from a seismic airgun source as specified in a Joint Industry Programme benchmark problem. Similar analyses are carried out based on at-sea acoustic measurements on the continental shelf, made with a vector sensor positioned 1.45 m off the seabed. These measurements are on transient time-limited signals from multiple underwater explosive sources at differing ranges, and from a towed, sonar source. The study demonstrates, both numerically and experimentally, that under many realistic scenarios, kinematic based acoustic dosage metrics within the water column can be…

In speech production research, talkers often perform a speech task several times per recording session with different speaking styles or in different environments. For example, Lombard speech studies typically have talkers speak in several different noise conditions. However, it is unknown to what degree simple repetition of a speech task affects speech acoustic characteristics or whether repetition effects might offset or exaggerate effects of speaking style or environment. The present study assessed speech acoustic changes over four within-session repetitions of a speech production taskset performed with two speaking styles recorded in separate sessions: conversational and clear speech. In each style, ten talkers performed a set of three speech tasks four times. Speaking rate, median fundamental frequency, fundamental frequency range, and mid-frequency spectral energy for read sentences were measured and compared across test blocks both within-session and between the two styles. Results indicate that statistically significant changes can occur from one repetition of a speech task…

This study devised a physics-informed neural network (PINN) framework to solve the wave equation for acoustic resonance analysis. The proposed analytical model, ResoNet, minimizes the loss function for periodic solutions and conventional PINN loss functions, thereby effectively using the function approximation capability of neural networks while performing resonance analysis. Additionally, it can be easily applied to inverse problems. The resonance in a one-dimensional acoustic tube, and the effectiveness of the proposed method was validated through the forward and inverse analyses of the wave equation with energy-loss terms. In the forward analysis, the applicability of PINN to the resonance problem was evaluated via comparison with the finite-difference method. The inverse analysis, which included identifying the energy loss term in the wave equation and design optimization of the acoustic tube, was performed with good accuracy.

Acoustic black holes (ABHs) are known as efficient structural dampers. Periodic lattices are identified as an efficient way to forbidden wave propagation in targeted frequency bandgaps (BGs). The paper demonstrates the possibility to merge the ABH effect with Bragg BGs. The geometrical layout leading to this double effect consists of a plate of periodically modulated thickness by a combination of cosine functions of the spatial coordinates constituting an ABH-like cell, coated with a thin damping layer. The resulting metamaterial allows the realization of solid, stiff, and nonresonant panels over a wide frequency range, including low frequencies, without increasing the mass. First, the band structure is analyzed in the conservative case (without damping layer) using a plane wave expansion model following Kirchhoff's assumptions. The results show the existence of low-frequency BGs that can be controlled by only three geometric parameters, which are defined on the type of lattice chosen (square or hexagonal). Next, a finite size panel is designed for the hexagonal lattice.…

The probability distribution of three-dimensional sound speed fields (3D SSFs) in an ocean region encapsulates vital information about their variations, serving as valuable data-driven priors for SSF inversion tasks. However, learning such a distribution is challenging due to the high dimensionality and complexity of 3D SSFs. To tackle this challenge, we propose employing the diffusion model, a cutting-edge deep generative model that has showcased remarkable performance in diverse domains, including image and audio processing. Nonetheless, applying this approach to 3D ocean SSFs encounters two primary hurdles. First, the lack of publicly available well-crafted 3D SSF datasets impedes training and evaluation. Second, 3D SSF data consist of multiple 2D layers with varying variances, which can lead to uneven denoising during the reverse process. To surmount these obstacles, we introduce a novel 3D SSF dataset called 3DSSF, specifically designed for training and evaluating deep generative models. In addition, we devise a high-capacity neural architecture for the diffusion model to…

Piezoelectric composite materials (PCMs) with shunt damping circuits are used widely in hydroacoustics because of the flexible adjustability of their parameters. PCMs offer good underwater sound absorption, but shortcomings remain, such as poor low-frequency sound absorption, narrow bandwidth, and a single dissipation mechanism. In this paper, the tunable underwater sound absorption of a 0-3 PCM combined with a cavity structure and shunt circuit (PCMC) is studied systematically. First, the equivalent material parameters of 0-3 PCM are derived based on the Yamada model, and then a theoretical electroacoustic model is established for solving the absorption coefficient and is mutually verified with the numerical simulation method. On this basis, the tunable absorption characteristics of the structure are analyzed. The results show that coupling the energy dissipation mechanism of 0-3 PCM with the acoustic mechanism of the cavity structure not only achieves strong absorption at lower frequencies but also enriches the absorption mode in the mid-high frequencies by connecting the shunt…

Reverberation is the main background interference for active sonar detection in shallow sea. Reverberation suppression is crucial for enhancing the performance of active sonar. In this paper, a reverberation suppression method based on low-rank sparse decomposition is proposed. First, both the sparseness property of the targets and the non-local self-correlation property of the reverberation are used to construct a range azimuth patch matrix model. The reverberation suppression problem is then transformed into an optimization problem for the recovery of a low-rank sparse matrix. The validity of the proposed method is verified by using the measured data. Results show that, compared with the reverberation pre-whitening and sparse fractional Fourier transform methods, the proposed method significantly improves the reverberation suppression performance and achieves a better detection result when the signal-to-interference ratio is below -2 dB.

Humans are sensitive to air-borne sound as well as to mechanical vibrations propagating in solids in the frequency range below 20 kHz. Therefore, the development of multifunctional filters for both vibration reduction and sound insulation within the frequency range of human sensitivity is a research topic of primary interest. In this paper, a high-contrast biphasic mechanical metamaterial, composed of periodic elastic solid cells with air-filled voids, is presented. By opening intercellular air-communicating channels and introducing channel-bridging solid-solid couplings, the frequency dispersion spectrum of the metamaterial can be modified to achieve complete and large bandgaps for acoustic and elastic waves. From a methodological viewpoint, the eigenproblem governing the free wave propagation is solved using a hybrid analytical-computational technique, while the waveform classification is based on polarization factors expressing the fraction of kinetic and elastic energies stored in the solid and fluid phases. Based on these theoretical results, a mechanical metafilter consisting…

In deep water, deploying a short vertical line array (VLA) is an effective way for source localization. In the past decade, most studies focused on localizing sources at the short to moderate ranges in the reliable acoustic path or the direct arrival zone (DAZ), with a VLA deployed near the ocean bottom. Little work has been done for the end part of the DAZ and the zones outside the DAZ. In addition, a VLA deployed at other depths rather than near the bottom is rarely studied. This paper proposes a near-surface source depth estimation method by matching the measured time delay with a library of modeled values under different source depths calculated by a simple formula. This method is suitable for zones, which contains two paths (one is reflected from the sea surface) with very close arrival angles, of a VLA deployed not only near the bottom, but also at other depths of the water column. Source depth estimation strategy for the end part of each zone, which faces the problem of poor depth resolution, is also analyzed. Simulation and experimental data of the airgun and explosive…

Because a reference signal is often unavailable in real-world scenarios, reference-free speech quality and intelligibility assessment models are important for many speech processing applications. Despite a great number of deep-learning models that have been applied to build non-intrusive speech assessment approaches and achieve promising performance, studies focusing on the hearing impaired (HI) subjects are limited. This paper presents HASA-Net+, a multi-objective non-intrusive hearing-aid speech assessment model, building upon our previous work, HASA-Net. HASA-Net+ improves HASA-Net in several ways: (1) inclusivity for both normal-hearing and HI listeners, (2) integration with pre-trained speech foundation models and fine-tuning techniques, (3) expansion of predictive capabilities to cover speech quality and intelligibility in diverse conditions, including noisy, denoised, reverberant, dereverberated, and vocoded speech, thereby evaluating its robustness, and (4) validation of the generalization capability using an out-of-domain dataset.

Small vessels (<10 m) radiate underwater noise in sensitive coastal environments, but there is insufficient knowledge of their noise radiation. Through detailed measurements of seven small boats and a jet ski in a shallow water environment on the Swedish west coast, this study presents results on the speed dependence of small vessel underwater noise, its sources, and its directivity. For vessels with planing hulls, broadband source levels increase with speed until planing is attained. When planing, source level dependence on speed is weak. A detailed noise source analysis of one vessel showed that at low speed, tonals from the engine dominate the noise radiation, whereas at higher speeds, propeller tonals and broadband noise dominate. Noise radiation into different horizontal angles shows little angle dependence, and noise levels relative to the closest point of approach show a similar pattern across all investigated vessels. Received noise levels at approximately 100 m range are not high enough to cause hearing impairment in marine animals, but fast-moving small vessels may cause…

Active road noise control (ARNC) systems have been widely investigated for low-frequency road noise attenuation in vehicle cabins. Multiple reference and error sensors are usually required to ensure noticeable noise reduction. However, this tends to slow down the convergence speed of adaptive algorithms due to the coupling of secondary paths and the cross correlation of reference signals. Furthermore, the high computational burden of normally utilized multichannel control algorithms exacerbates the difficulty of practical implementations. In this paper, an online decoupling-whitening frequency domain filtered-error least mean square (ODW-FDFeLMS) algorithm is proposed to address the aforementioned problems. Secondary path decoupling through inner-outer product decomposition and online reference whitening through spectral factorization effectively accelerate the convergence rate. Additionally, the utilization of the filtered-error algorithm based on frequency domain processing mitigates the computational complexity. Simulations with measured road noise data confirm the superiority of…

The ocean soundscape is a complex superposition of sound from natural and anthropogenic sources. Recent advances in acoustic remote sensing and marine bioacoustics have highlighted how animals use their soundscape and how the background sound levels are influenced by human activities. In this paper, developments in computational ocean acoustics, remote sensing, and oceanographic modeling are combined to generate modelled sound fields at multiple scales in time and space. Source mechanisms include surface shipping, surface wind, and wave fields. A basin scale model is presented and applied to the United States Atlantic Outer Continental Shelf (OCS). For model-data comparison at a single hydrophone location, the model is run for a single receiver position. Environmental and source model uncertainty is included in the site-specific modeling of the soundscape. An inversion of the local sediment type is made for a set of sites in the OCS. After performing this inversion, the qualitative comparison of the modelled sound pressure level (SPL) time series and observed SPL is excellent. The…

Recently, acoustic communication employing orbital angular momentum (OAM) opens another avenue for efficient data transmission in aquatic environments. Current topological charge (TC) detection of OAM beams relies on the orthogonality among different-order OAM beams. However, such strategy requires measurements of the complete azimuthal acoustic pressure, which inevitably reduces the efficiency and increases the bit error rate (BER). To address these challenges, this study proposes a modified dynamic modal decomposition (DMD) method by partially sampling the acoustic field for precise TC detection. Numerical simulations confirm the accuracy of this approach in extracting single or multiple TCs magnitudes within a partially sampled acoustic field. We theoretically compare the performance of the modified DMD approach with conventional orthogonal decoding method. Simulation results indicate that our modified DMD scheme exhibits lower BER under the same noise interference and is more robust to the array misalignment. This research introduces an efficient demodulation solution for…

Reconstructing a three-dimensional ocean sound speed field (SSF) from limited and noisy measurements presents an ill-posed and challenging inverse problem. Existing methods used a number of pre-specified priors (e.g., low-rank tensor and tensor neural network structures) to address this issue. However, the SSFs are often too complex to be accurately described by these pre-defined priors. While utilizing neural network-based priors trained on historical SSF data may be a viable workaround, acquiring SSF data remains a nontrivial task. This work starts with a key observation: Although natural images and SSFs admit fairly different characteristics, their denoising processes appear to share similar traits-as both remove random components from more structured signals. This observation allows us to incorporate deep denoisers trained using extensive natural images to realize zero-shot SSF reconstruction, without any extra training or network modifications. To implement this idea, an alternating direction method of multipliers (ADMM) algorithm using such a deep denoiser is proposed, which…

This study discusses acoustic dissipation, which contributes to inaccuracies in impedance tube measurements. To improve the accuracy of these measurements, this paper introduces a transfer function model that integrates diverse dissipation prediction models. Bayesian inference is used to estimate the important parameters included in these models, describing dissipation originating from various mechanisms, sound speed, and microphone positions. By using experimental measurements and considering a hypothetical air layer in front of a rigid termination as the material under test, Bayesian parameter estimation allows a substantial enhancement in characterization accuracy by incorporating the dissipation and sound speed estimates. This approach effectively minimizes residual absorption coefficients attributed to both boundary-layer effects and air medium relaxation. The incorporation of dissipation models leads to a substantial reduction (to 1%) in residual absorption coefficients. Moreover, the use of accurately estimated parameters further enhances the accuracy of actual tube…

The study of humpback whale song using passive acoustic monitoring devices requires bioacousticians to manually review hours of audio recordings to annotate the signals. To vastly reduce the time of manual annotation through automation, a machine learning model was developed. Convolutional neural networks have made major advances in the previous decade, leading to a wide range of applications, including the detection of frequency modulated vocalizations by cetaceans. A large dataset of over 60 000 audio segments of 4 s length is collected from the North Atlantic and used to fine-tune an existing model for humpback whale song detection in the North Pacific (see Allen, Harvey, Harrell, Jansen, Merkens, Wall, Cattiau, and Oleson (2021). Front. Mar. Sci. 8, 607321). Furthermore, different data augmentation techniques (time-shift, noise augmentation, and masking) are used to artificially increase the variability within the training set. Retraining and augmentation yield F-score values of 0.88 on context window basis and 0.89 on hourly basis with false positive rates of 0.05 on context…

Automotive ultrasonic sensors come into play for close-range surround sensing in parking and maneuvering situations. In addition to ultrasonic ranging, classifying obstacles based on ultrasonic echoes to improve environmental perception for advanced driver-assistance systems is an ongoing research topic. Related studies consider only magnitude-based features for classification. However, the phase of an echo signal contains relevant information for target discrimination. This study discusses and evaluates the relevance of the target phase in echo signals for object classification in automotive ultrasonic sensing based on lab and field measurements. Several phase-aware features in the time domain and time-frequency features based on the continuous wavelet transform are proposed and processed using a convolutional neural network. Indeed, phase features are found to contain relevant information, producing only 4% less classification accuracy than magnitude features when the phase is appropriately processed. The investigation reveals high redundancy when magnitude and phase features are…

The problem of characterizing voice quality has long caused debate and frustration. The richness of the available descriptive vocabulary is overwhelming, but the density and complexity of the information voices convey lead some to conclude that language can never adequately specify what we hear. Others argue that terminology lacks an empirical basis, so that language-based scales are inadequate a priori. Efforts to provide meaningful instrumental characterizations have also had limited success. Such measures may capture sound patterns but cannot at present explain what characteristics, intentions, or identity listeners attribute to the speaker based on those patterns. However, some terms continually reappear across studies. These terms align with acoustic dimensions accounting for variance across speakers and languages and correlate with size and arousal across species. This suggests that labels for quality rest on a bedrock of biology: We have evolved to perceive voices in terms of size/arousal, and these factors structure both voice acoustics and descriptive language. Such…

Understanding the ultrasound pressure-driven dynamics of microbubbles confined in viscoelastic materials is relevant for multiple biomedical applications, ranging from contrast-enhanced ultrasound imaging to ultrasound-assisted drug delivery. The volumetric oscillations of spherical bubbles are analyzed using the Rayleigh-Plesset equation, which describes the conservation of mass and momentum in the surrounding medium. Several studies have considered an extension of the Rayleigh-Plesset equation for bubbles embedded into viscoelastic media, but these are restricted to a particular choice of constitutive model and/or to small deformations. Here, we derive a unifying equation applicable to bubbles in viscoelastic media with arbitrary complex moduli and that can account for large bubble deformations. To derive this equation, we borrow concepts from finite-strain theory. We validate our approach by comparing the result of our model to previously published results and extend it to show how microbubbles behave in arbitrary viscoelastic materials. In particular, we use our viscoelastic…

The loss of Arctic sea ice is one of the most visible signs of global climate change. As Arctic sea ice has retreated, Arctic marine shipping has increased. The Pan-Arctic's unique underwater acoustic properties mean that even small increases in ship traffic can have a significant effect on the ambient soundscape. This study presents the first long-term, basin-scale model of shipping noise in the Pan-Arctic with a focus on a few select sub-regions. The Arctic Ship Traffic Database from the Protection of the Arctic Marine Environment is used in this study to model the locations and source levels from ships operating in the Pan-Arctic between 2013 and 2019. The acoustic footprint of these ships is explored temporally for the entire basin as well as for the select large maritime ecosystems of the Barents Sea, the Northern Bering-Chukchi Sea, and Baffin Bay. From 2013 to 2019, modeled shipping noise propagating underwater broadly increased between 5-20 dB across the Pan-Arctic, but more specific results in sub-regions are presented and discussed.

Distributed acoustic sensing (DAS), converting fiber-optic cables into dense acoustic sensors, is a promising technology that offers a cost-effective and scalable solution for long-term, high-resolution studies in ocean acoustics. In this paper, the telecommunication cable of Martha's Vineyard Coastal Observatory (MVCO) is used to explore the feasibility of cable localization and shallow-water sound propagation with a mobile acoustic source. The MVCO DAS array records coherent, high-quality acoustic signals in the frequency band of 105-160 Hz, and a two-step inversion method is used to improve the location accuracy of DAS channels, reducing the location uncertainty to ∼2 m. The DAS array with refined channel positions enables the high-resolution observation of acoustic modal interference. Numerical simulations that reproduce the observed interference pattern suggest a compressional speed of 1750 m/s in the sediment, which is consistent with previous in situ geoacoustic measurements. These findings demonstrate the long-term potential of DAS for high-resolution ocean acoustic studies.

Air-conducted (AC) microphones capture the high-quality desired speech and ambient noise, whereas bone-conducted (BC) microphones are immune to ambient noise but only capture band limited speech. This paper proposes a speech enhancement model that leverages the merits of BC and AC speech. The proposed model takes the spectrogram of BC and AC speech as input and fuses them by an attention-based feature fusion module. The backbone network of the proposed model uses the fused signals to estimate mask of the target speech, which is then applied to the noisy AC speech to recover the target speech. The proposed model adopts a lightweight design of densely gated convolutional attention network (DenGCAN) as the backbone network, which contains encoder, bottleneck layers, and decoder. Furthermore, this paper improves an attention gate and integrates it into skip-connections of DenGCAN, which allows the decoder to focus on the key areas of the feature map extracted by the encoder. As the DenGCAN adopts self-attention mechanism, the proposed model has the potential to improve noise reduction…

For cochlear implant (CI) listeners, holding a conversation in noisy and reverberant environments is often challenging. Deep-learning algorithms can potentially mitigate these difficulties by enhancing speech in everyday listening environments. This study compared several deep-learning algorithms with access to one, two unilateral, or six bilateral microphones that were trained to recover speech signals by jointly removing noise and reverberation. The noisy-reverberant speech and an ideal noise reduction algorithm served as lower and upper references, respectively. Objective signal metrics were compared with results from two listening tests, including 15 typical hearing listeners with CI simulations and 12 CI listeners. Large and statistically significant improvements in speech reception thresholds of 7.4 and 10.3 dB were found for the multi-microphone algorithms. For the single-microphone algorithm, there was an improvement of 2.3 dB but only for the CI listener group. The objective signal metrics correctly predicted the rank order of results for CI listeners, and there was an…