Journal of the Acoustical Society of America最新文献

Ambient sound was continuously recorded for 52 days by three synchronized, single-hydrophone, near-bottom receivers. The receivers were moored at depths of 2573, 2994, and 4443 m on flanks and in a trough between the edifices of the Atlantis II seamounts. The data reveal the power spectra and intermittency of the ambient sound intensity in a 13-octave frequency band from 0.5 to 4000 Hz. Statistical distribution of sound intensity exhibits much heavier tails than in the expected exponential intensity distribution throughout the frequency band of observations. It is established with high statistical significance that the data are incompatible with the common assumption of normally distributed ambient noise in deep water. Spatial variability of the observed ambient sound appears to be controlled by the seafloor properties, bathymetric shadowing, and nonuniform distribution of the noise sources on the sea surface. Temporal variability of ambient sound is dominated by changes in the wind speed and the position of the Gulf Stream relative to the experiment site. Ambient sound intensity increases by 4-10 dB when the Gulf Stream axis is within 25 km from the receivers. The sound intensification is attributed to the effect of the Gulf Stream current on surface wave breaking.

Ocean gliders are versatile and efficient passive acoustic monitoring platforms in remote marine environments, but few studies have examined their potential to monitor ship underwater noise. This study investigates a Slocum glider's capability to assess ship noise compared to the ability of fixed observers. Trials were conducted in shallow coastal inlets and deep bays in Newfoundland, Canada, using a glider, hydrophone array, and single-moored system. The study focused on (1) the glider's self-noise signature, (2) range-depth-dependent propagation loss (PL) models, and (3) identifying the location of the vessel to the glider using glider acoustic measurements. The primary contributors to the glider's self-noise were the buoyancy pump and rudder. The pitch-motor noise coincided with the buoyancy pump activation and did not contribute to the glider self-noise in our experiments. PL models showed that seafloor bathymetry and sound speed profiles significantly impacted estimates compared to models assuming flat and range-independent profiles. The glider's performance in recording ship noise was superior to that of other platforms. Using its hydrophones, the glider could identify the bearing from the vessel, although a third hydrophone would improve reliability and provide range. The findings demonstrate that gliders can characterize noise and enhance our understanding of ocean sound sources.

Theoretical modeling and parameter identification are essential for optimizing loudspeaker performance and enabling active control. Although relevant theories for moving-coil loudspeakers are well-developed, accurate theoretical modeling and parameter identification methods for balanced armature loudspeakers (BALs) are scant. This study proposes a model using the equivalent circuit method (ECM) for BALs, with consideration of the armature-suspension coupling as well as the non-piston vibration of the diaphragm. Based on the proposed ECM model, a time-domain identification algorithm utilizing measured voltage, current, and displacement data is established to identify the necessary parameters. Employing the theoretical model and proposed identification method, the model parameters of two different BALs are measured. Comparisons between experimental and numerical results demonstrate the accuracy and effectiveness of the proposed model and identification method in predicting impedance, displacement, and sound pressure responses.

Soundscapes are an important part of urban landscapes and play a key role in the health and well-being of citizens. However, predicting soundscapes over a large area with fine resolution remains a great challenge and traditional methods are time-consuming and require laborious large-scale noise detection work. Therefore, this study utilized machine learning algorithms and street-view images to estimate a large-area urban soundscape. First, a computer vision method was applied to extract landscape visual feature indicators from large-area streetscape images. Second, the 15 collected soundscape indicators were correlated with landscape visual indicators to construct a prediction model, which was applied to estimate large-area urban soundscapes. Empirical evidence from 98 000 street-view images in Fuzhou City indicated that street-view images can be used to predict street soundscapes, validating the effectiveness of machine learning algorithms in soundscape prediction.

This paper presents an equivalent source method (ESM) for analyzing sound propagation in small-scale acoustic structures with thermoviscous effects. The formulations that describe the thermal, viscous, and acoustic modes for thermoviscous acoustic problems are introduced. The concept of ESM is then applied to solve these formulations, resulting in an efficient numerical computation and implementation procedure. Based on two different strategies, the obtained ESM formulations are coupled at the boundary using the isothermal, non-slip, and null-divergence conditions. The coupling based on the first strategy is efficient for solving thermoviscous acoustic problems with few matrices required. However, this procedure faces the evaluation of the tangential derivatives of the boundary velocity. Coupling the ESM formulations directly for each component of the total particle velocity at the boundary has no such problem, which leads to the second strategy. However, it entails a larger memory usage compared to the former. Additionally, the coupled finite element method (FEM)-ESM formulations based on the above strategies are developed for acoustic-structural interaction. The validity of the presented ESM formulations is demonstrated through benchmark examples, and that of the coupled FEM-ESM formulation is illustrated by the numerical analysis of a simplified microphone.

Measurement and analysis of the continuous and intermittent noise produced by armored vehicle (AV) platforms, including the output from communications systems as experienced by crew, are necessary for the purposes of exposure prediction, to support the selection of hearing protection and communication devices, and to facilitate assessments of compliance with occupational health and safety legislation. Practical estimation of the personal noise exposure of AV crews requires the assessment of the vehicle, communications and special-to-role activity noise sources, and an understanding of how these sources combine. Procedures are described that consider instrumentation requirements, AV configuration and build standard, operating conditions representative of actual use, the application of speed thresholding to measurements, and derivation of communications noise levels. Real-world examples are given where these procedures have been applied to an in-service tracked AV to estimate crew noise exposure. The procedures and methods presented are a compromise between precision, repeatability, reproducibility, and pragmatism. Measurements of AV noise are expected to be obtained during the commissioning stage of vehicle design, immediately prior to the vehicle being put into operational service and following any major modifications to the vehicle to inform the necessary engineering, administrative, and personal protective equipment control measures.

Acoustical parameter estimation is a routine task in many domains. The performance of existing estimation methods is affected by external uncertainty, yet the methods provide no measure of confidence in the estimates. Hence, it is crucial to quantify estimate uncertainty before real-world deployment. Conformal prediction (CP) generates statistically valid prediction intervals for any estimation model using calibration data; a limitation is that calibration data needed by CP must come from the same distribution as the test-time data. In this work, we propose to use CP to obtain statistically valid uncertainty intervals for acoustical parameter estimation using a data-driven model or an analytical model without training data. We consider direction-of-arrival estimation and localization of sources. The performance is validated on plane wave data with different sources of uncertainty, including ambient noise, interference, and sensor location uncertainty. The application of CP for data-driven and traditional propagation models is demonstrated. Results show that CP can be used for statistically valid uncertainty quantification with proper calibration data.

Language communicators use acoustic-phonetic cues to convey a variety of social information in the spoken language, and the learning of a second language affects speech production in a social setting. It remains unclear how speaking different dialects could affect the acoustic metrics underlying the intended communicative meanings. Nine Chinese Bayannur-Mandarin bidialectics produced single-digit numbers in statements of both Standard Mandarin and the Bayannur dialect with different levels of intended confidence. Fifteen listeners judged the intention presence and confidence level. Prosodically unmarked and marked stimuli exhibited significant differences in perceived intention. A higher intended level was perceived as more confident. The acoustic analysis revealed the segmental (third and fourth formants, center of gravity), suprasegmental (mean fundamental frequency, fundamental frequency range, duration), and source features (harmonic to noise ratio, cepstral peak prominence) can distinguish between confident and doubtful expressions. Most features also distinguished between dialect and Mandarin productions. Interactions on fourth formant and mean fundamental frequency suggested that speakers made greater use of acoustic parameters to encode confidence and doubt in the Bayannur dialect than in Mandarin. In machine learning experiments, the above-chance-level overall classification rates for confidence and doubt and the in-group advantage supported the dialect theory.

Bat call signal analysis is an important research topic, which is meaningful for bat species identification, and the design of various biomimetic systems. In addition to the commonly used methods in the time-frequency domain, the fractional Fourier transform (FRFT) is a valuable signal processing tool, as it is a generalization of the Fourier transform. However, the FRFT is constrained to the analysis of the linear frequency modulated-like bat call signal, while the modulation of the harmonics in a bat call is often nonlinear. For this reason, this paper proposes an integral transform, named matched power-frequency-modulated (PFM) signal transform (MPST), which is also the generalization of the Fourier transform, more precisely, a time-warping Fourier transform. As with the limitation of FRFT, the MPST is constrained to the analysis of the PFM-like bat call with the instantaneous frequency defined as an approximate power function abut time, in which the power can be an arbitrary positive integer or a fraction. The applications of MPST on the PFM-modeled bat call analysis are mainly parameter estimation and harmonic separation, and the performance is fully validated using the recordings of the feeding buzzes, social calls, and distress calls from the European bats.

Numerical solutions to the parabolic wave equation are plagued by the curse of dimensionality coupled with the Nyquist criterion. As a remedy, a new range-dynamical low-rank split-step Fourier method is developed. The integration scheme scales sub-linearly with the number of classical degrees of freedom in the transverse directions. It is orders of magnitude faster than the classic full-rank split-step Fourier algorithm and saves copious amounts of storage space. This enables numerical solutions of the parabolic wave equation at higher frequencies and on larger domains, and simulations may be performed on laptops rather than high-performance computing clusters. Using a rank-adaptive scheme to optimize the low-rank equations further ensures the approximate solution is highly accurate and efficient. The methodology and algorithms are demonstrated on realistic high-resolution data-assimilative ocean fields in Massachusetts Bay for two three-dimensional acoustic configurations with different source locations and frequencies. The acoustic pressure, transmission loss, and phase solutions are analyzed in the two geometries with seamounts and canyons across and along Stellwagen Bank. The convergence with the rank of the subspace and the properties of the rank-adaptive scheme are demonstrated, and all results are successfully compared with those of the full-rank method when feasible.