Journal of the Acoustical Society of America最新文献

Soundscape studies vary considerably in study design, statistical methods, and model fit metrics used. Due to this confounding of data and methods, it is difficult to assess the suitability of statistical modelling techniques used in the literature. Therefore, five different methods and two performance metrics were applied to three existing soundscape datasets to model soundscape Pleasantness and Eventfulness based on seven acoustic and three sociodemographic predictors. Datasets differed in soundscape type (urban outdoor vs indoor), experimental setting (field- vs lab-based), size, and study design (site- vs person-centered). The fixed-effects and mixed-effects methods ranged from linear to nonlinear regression based on advanced machine learning approaches. Results showed that models performed better for Eventfulness than for Pleasantness in most cases, while performance as measured by the out-of-sample R2 was dependent on the total variance of the target, especially in both field studies with imbalanced targets and groups. Nonlinear methods consistently outperformed linear regression, with random forest and extreme gradient boosting performing particularly well, while the performance levels of all nonlinear methods remained comparable. Mixed-effects models provided a more generalized, albeit slightly smaller prediction performance when tested on unknown groups. Finally, this study motivates the use of cross-validation with special splitting for analyzing small imbalanced datasets.

The World Health Organization Environmental Noise Guidelines provide source-based nighttime sound level (Lnight) recommendations. For non-aircraft sources, the recommended Lnight is where the absolute prevalence of high sleep disturbance (HSD) equals 3%. The Guideline Development Group did not provide an Lnight for wind turbines due to inadequate data. In the current study, calculated outdoor wind turbine Lnight levels ranged from <20.5 to 41.5 dB(A). Between May and September 2013, questionnaires were completed by 606 males and 632 females, 18-79 years of age, randomly selected from households 0.25 to 11.22 km from operational wind turbines. When the source of sleep disturbance was unspecified, the mean prevalence of HSD was 13.3% overall and unrelated to Lnight (p = 0.53). As Lnight increased, identifying wind turbines as one of the causes of HSD increased from 0% below 20.5 dB(A) to 3.8% between 35.5-41.5 dB(A) (p = 0.01). The 3%HSD benchmark was observed where Lnight was 33.5 dB(A) [95% confidence interval (CI) 31.1-36.1 dB(A)]. Results affirm findings from Health Canada's Community Noise and Health Study of minimal impacts of wind turbines on sleep [Michaud et al. (2016a). "Effects of wind turbine noise on self-reported and objective measures of sleep," Sleep 39(1), 97-109], yet noted uncertainties and limitations are discussed, including the suggestion that the HSD benchmark for wind turbines may be too low.

This study investigated the effects of spaced and massed repetition on the acquisition and retention of English /b/ and /p/ sounds among Saudi English as a foreign language (EFL) learners. Forty-nine university students participated in a quasi-experimental design comparing two learning conditions: massed repetition and spaced repetition. Participants were taught the pronunciation of 40 new English words containing the target sounds through video-based instruction. The experiment utilized a within-subjects design, where each participant in both the control and experimental groups was exposed to a total of 40 words: 20 words taught using massed repetition and 20 words taught using spaced repetition. Pronunciation performance was assessed through tests administered immediately after the treatment (Time 1) and after 4 weeks post-treatment (Time 2). The study also explored the interaction between repetition type and input variability by incorporating High Variability Phonetic Training and Low Variability Phonetic Training conditions. Results revealed significant differences between spaced and massed repetition, with spaced repetition showing superior outcomes in both immediate and delayed tests. The study revealed no significant differences were found between High Variability Phonetic Training and Low Variability Phonetic Training conditions. These findings contribute to our understanding of effective pronunciation training methods in EFL contexts and highlight the importance of distributed practice in language learning.

Many animal species are known to show individuality in their acoustic communication. This variation in individual male signatures can be decisive for female choice. Within the damselfishes, Dascyllus species are known for prolific sound production during the realization of movements associated with courtship (i.e., the signal jump) and spawning (mating sounds). However, the ability to distinguish the individuality of male Dascyllus sound signatures is unknown. We investigated the variability in the courtship and mating sounds of 17 males of Dascyllus albisella at Johnston Atoll, Central Pacific Ocean, to determine whether it was possible to distinguish them from one another and thus have information on their ability to convey individual information. Acoustic analyses confirmed that courtship sounds differed from mating sounds. Comparative analyses suggest that acoustic signals cannot serve as distinctive traits unless the individuals are of different sizes. Males of D. albisella do not use individual signatures in a reproductive behavioral context. However, it cannot be ruled out that variations in the sound production rate may serve as a discriminative feature.

During the 2016-2017 Canada Basin Acoustic Propagation Experiment, an ocean acoustic tomography array with a radius of 150 km measured the impulse responses of the ocean every 4 hr at a variety of ranges and bearings using broadband signals with center frequencies from 172.5 to 275 Hz. Ice-profiling sonar data showed a gradual increase in ice draft over the winter with daily median ice drafts reaching maxima of about 1.5 m and daily standard deviations reaching maxima of about 1.2 m. The travel-time variability of early, resolved arrivals from refracted-surface-reflected rays with lower turning depths below 500 m was reported in a previous paper [Worcester et al. (2023). J. Acoust. Soc. Am. 153, 2621-2636]. Here, the transmission loss of these same ray arrivals is analyzed. The transmission loss was lowest when open water was present and increased as the ice draft increased. The excess transmission loss per surface reflection, defined as the increase in transmission loss relative to open water conditions, increased with center frequency and surface grazing angle. The combination of transmission loss measurements for resolved ray arrivals and ice drafts from the ice-profiling sonars provides an excellent dataset for testing ice-scattering models.

Matched-field processing (MFP) achieves underwater source localization by measuring the correlation between the array and replica signals, with traditional MFP being equivalent to estimating the Euclidean distance between the data cross-spectral density matrix (CSDM) and replica matrices. However, in practical applications, random inhomogeneities in the marine environment and inaccurate estimation of CSDM reduce MFP performance. The traditional minimum variance matched-field processor with environmental perturbation constraints perturbs a priori environment parameters to obtain linear constraints and yields the optimal weight vectors as the replica vectors. In this paper, within the framework of information geometry, the geometric properties of CSDMs as semi-positive definite and Hermitian enable CSDMs to be described as points in a Riemannian manifold. Source localization can be achieved by quantifying the similarity between the CSDMs as the geodesic distance between the points on the manifold. This paper introduces a constrained replica CSDM composed of perturbed replica vectors and proposes a robust matched-field processor based on two non-Euclidean distances: the Riemannian distance and the modified Jensen-Shannon distance. Simulations and experimental results demonstrate that the proposed processors are more robust against environmental and statistical mismatches than traditional processors and can also reduce sidelobe level and improve the resolution.

Passive acoustic monitoring (PAM) is an increasingly popular tool to study vocalising species. The amount of data generated by PAM studies calls for robust automatic classifiers. Deep learning (DL) techniques have been proven effective in identifying acoustic signals in challenging datasets, but due to their black-box nature their underlying biases are hard to quantify. This study compares human analyst annotations, a multi-hypothesis tracking (MHT) click train classifier and a DL-based acoustic classifier to classify acoustic recordings based on the presence or absence of sperm whale (Physeter macrocephalus) click trains and study the temporal and spatial distributions of the Mediterranean sperm whale subpopulation around the Balearic Islands. The MHT and DL classifiers showed agreements with human labels of 85.7% and 85.0%, respectively, on data from sites they were trained on, but both saw a drop in performance when deployed on a new site. Agreement rates between classifiers surpassed those between human experts. Modeled seasonal and diel variations in sperm whale detections for both classifiers showed compatible results, revealing an increase in occurrence and diurnal activity during the summer and autumn months. This study highlights the strengths and limitations of two automatic classification algorithms to extract biologically useful information from large acoustic datasets.

This article presents a spatial environmental inversion scheme using broadband impulse signals with deep learning (DL) to model a single spatially-varying sediment layer over a fixed basement. The method is applied to data from the Seabed Characterization Experiment 2022 (SBCEX22) in the New England Mud-Patch (NEMP). Signal Underwater Sound (SUS) explosive charges generated impulsive signals recorded by a distributed array of bottom-moored hydrophones. The inversion scheme is first validated on a range-dependent synthetic test set simulating SBCEX22 conditions, then applied to experimental data to predict the lateral spatial structure of sediment sound speed and its ratio with the interfacial water sound speed. Traditional geoacoustic inversion requires significant computational resources. Here, a neural network enables rapid single-signal inversion, allowing the processing of 1836 signals along 722 tracks. The method is applied to both synthetic and experimental data. Results from experimental data suggest an increase in both absolute compressional sound speed and sound speed ratio from southwest to northeast in the NEMP, consistent with published coring surveys and geoacoustic inversion results. This approach demonstrates the potential of DL for efficient spatial geoacoustic inversion in shallow water environments.

Flow-induced noise is a complex source that significantly impacts submarines' stealth performance. While previous studies have provided valuable insights into the acoustic radiation of scaled-down submarine models, addressing the flow noise of full-scale prototypes has remained a daunting challenge. To bridge this gap, the research team undertook an extensive investigation to unveil the elusive similarity law of flow noise in both small and large-scale submarine models. By leveraging computational algorithms and turbulence models, the flow field of the submarine model was simulated, and the Kirchhoff and Ffowcs Williams-Hawkings model was employed to calculate the submarine's flow noise. This comprehensive study meticulously considered various influential factors, including Mach number, Reynolds number, etc., ultimately formulating a similarity correlation formula for submarine flow noise. The findings of this study revealed several key insights, including the minimal impact of accessories on submarine flow noise similarity, the adherence of the frequency of submarine flow noise to the Helmholtz number, and the intricate relationship between sound pressure level similarity law with Mach and Reynolds number. Ultimately, this study introduces and summarizes the submarine flow noise similarity law. This law enables the estimation of real-scale model flow noise by using small-scale model flow noise as a reference.

The ambient noise model of isotropic fields is not applicable to shallow-located platforms in the deep ocean, and the conventional methods for estimating the sound-ray-arrival-grazing-angle of targets do not account for the effects of surface noise. Therefore, target parameter estimation methods based on a single vector hydrophone are explored in this study. The approach used in this study integrates the hydroacoustic physical model, signal processing method, and ocean ambient. A time-domain model of the vector field of deep ocean ambient noise was developed for receivers positioned at shallow depths, followed by derivation of the covariance matrix of the single vector hydrophone based on this model. Subsequently, a target signal-to-noise ratio (SNR) estimation method using the covariance matrix derived from the single vector hydrophone was formulated. This method effectively addresses the challenge of distinguishing between target signal and noise by transforming the power estimation problem into a covariance matrix solving the task. Finally, a refined approach for estimating the sound-ray-arrival-grazing-angle of target is proposed, aiming to theoretically mitigate the impact of surface noise on the target signal. The experimental data obtained from a deep ocean region in the South China Sea indicate that the findings obtained using the method proposed in this study are consistent with the reference values derived from automatic identification system information. The method also demonstrates reliable estimation results even when the SNR exceeds -5 dB. The conceptual framework developed for SNR and sound-ray-arrival-grazing-angle estimation in this study can be readily applied to other ambient models, indicating potential applications in engineering field. The primary objective of the study was to enhance and augment underwater acoustic signal processing methods for shallow receivers deployed in the deep ocean.