Journal of the Acoustical Society of America最新文献

This study investigates the acoustic cues for listeners to differentiate checked syllables and tones from unchecked ones. In Xiapu Min, checked and unchecked syllables and tones differ in f0, glottalization, and duration, whereas these differences are reduced in their sandhi forms. In citation forms, listeners utilize all three cues while relying on duration the most. The results indicate that duration is an independent perceptual cue for checked syllables and tones, rather than a peripheral cue resulting from the syllable structure of /CVʔ/. In sandhi forms, where checked and unchecked syllables and tones are phonologically neutralized, the duration and f0 still influence listeners' perception of checked constituents significantly. Data from Xiapu Min, along with other languages, illustrate that cues consistently found in the production of checked syllables and tones are likely to be utilized in their perception.

Leading-edge serrations inspired by owls exhibit the capability to control airfoil-turbulence interaction noise, but the design principle of the serration shape is still an open issue. To this end, we designed five types of serration shapes with different combinations of curvature, namely, triangular, ogee, anti-ogee, feather-like, and anti-feather-like. These curves are applied to serrated modifications with different bluntness levels (sharp or blunt) and amplitudes (0.05, 0.075, and 0.1 chord length). Considering these serration shapes, 30 cases with various curved types, bluntness levels, and amplitudes are investigated using compressible large-eddy simulation and the acoustic analogy of Ffowcs-Williams and Hawkings on a rod-airfoil configuration. The outcomes reveal a general trend where increased amplitude and blunted serrations are more effective in noise mitigation. Notably, the blunt feather-like (FB) serrations demonstrate the maximum noise reduction capacity across all amplitude levels, decreasing the overall sound power level by up to 2.1 dB. Through multi-process acoustic analysis, source characteristics responsible for generating noise are diagnosed. It is found that noise reduction primarily stems from the change in the source distribution and destructive interference among sound sources, consistent with prior studies. Generally, the serration shape would significantly affect the source distribution and sound interference without altering the fundamental noise reduction mechanisms. The FB shape exhibits the highest concentration of sources at its peaks and roots among all shapes. The presence of concentrated sources in these locations enhances destructive interference, effectively reducing noise emissions. The superior noise-reduction feature of FB serrations should be attributed to both the concentration of sources and the destructive interference. This extensive examination underscores the importance of serration design, especially the potential of FB serrations, in noise control strategies for rod-airfoil configurations, contributing to advancements in aeroacoustic engineering.

Topological acoustic waveguides have a potential for applications in the precise transmission of sound. Currently, there is more attention to multi-band in this field. However, achieving tunability of the operating band is also of great significance. Different from previous studies, this paper proposes to replace the two-dimensional (2D) resonant cavity in the scatterer with an extended three-dimensional (3D) resonant cavity. In this way, a composite acoustic structure consisting of a 2D scatterer and a 3D resonant cavity is constructed. By controlling the position of the bottom of the resonant cavity, the length of the resonant cavities can be freely controlled. In this way, it is possible to achieve continuous control of the operating frequency band by a very simple mechanical method without changing the initial structure. The control range can reach nearly 6 kHz. This paper also proposes a parallel resonance mechanism that can increase the width of the bandgap by 50%. Simulation results show that this method does not affect the topological phase transition of the structure. In the transmission channel formed by two different topological phase interfaces of this topological acoustic waveguide, the acoustic wave has a high-precision unidirectional transmission characteristic that is immune to backscattering. This study provides a reliable solution for an ultra-wide range of controllable acoustic topological components.

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.

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.

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.

The amount of information contained in speech signals is a fundamental concern of speech-based technologies and is particularly relevant in speech perception. Measuring the mutual information of actual speech signals is non-trivial, and quantitative measurements have not been extensively conducted to date. Recent advancements in machine learning have made it possible to directly measure mutual information using data. This study utilized neural estimators of mutual information to estimate the information content in speech signals. The high-dimensional speech signal was divided into segments and then compressed using Mel-scale filter bank, which approximates the non-linear frequency perception of the human ear. The filter bank outputs were then truncated based on the dynamic range of the auditory system. This data compression preserved a significant amount of information from the original high-dimensional speech signal. The amount of information varied, depending on the categories of the speech sounds, with relatively higher mutual information in vowels compared to consonants. Furthermore, the information available in the speech signals, as processed by the auditory model, decreased as the dynamic range was reduced.

Rapid urban development impacts the integrity of tropical ecosystems on broad spatiotemporal scales. However, sustained long-term monitoring poses significant challenges, particularly in tropical regions. In this context, ecoacoustics emerges as a promising approach to address this gap. Yet, harnessing insights from extensive acoustic datasets presents its own set of challenges, such as the time and expertise needed to label species information in recordings. Here, this study presents an approach to investigating soundscapes: the use of a deep neural network trained on time-of-day estimation. This research endeavors to (1) provide a qualitative analysis of the temporal variation (daily and monthly) of the soundscape using conventional ecoacoustic indices and deep ecoacoustic embeddings, (2) compare the predictive power of both methods for time-of-day estimation, and (3) compare the performance of both methods for supervised classification and unsupervised clustering to the specific recording site, habitat type, and season. The study's findings reveal that conventional acoustic indices and the proposed deep ecoacoustic embeddings approach exhibit overall comparable performance. This article concludes by discussing potential avenues for further refinement of the proposed method, which will further contribute to understanding of soundscape variation across time and space.

Developing persistent and smart underwater markers is critical for improving navigation accuracy and communication capabilities of autonomous underwater vehicles (AUVs). A wireless acoustic identification tag, which uses a piezoelectric transducer tuned in the broadband ultrasonic range (200-500 kHz), was experimentally demonstrated to achieve highly efficient power transfer (source-to-tag electrical power efficiency of >2% at 6 m) and concurrent high data rate and backscatter level communication (>83.3 kbit s-1, >170 dB sound pressure level at 6 m) with potential operating range ≈ 10 m based on analytical extrapolations. Parameter selection considerations dictated by the desired range and data-rate requirements in communication are presented. The transducer piezoelectric element selection, impedance matching approach, and simulation-based circuit optimization for frequency multiplexed operation are also detailed. Experimental tests benchmarking performance sensitivity to source and tag misalignment are introduced and implications for AUV operations are discussed.