Journal of the Acoustical Society of America最新文献

Estimating a sound field in a region that includes sources (i.e., an inhomogeneous sound field) is challenging. This paper proposes the Gaussian process (GP) for estimating an inhomogeneous sound field in the case of anechoic condition. A kernel function is formulated as a weighted spatial correlation of free-field transfer functions in the modal domain. The weights for the kernel function are derived by introducing the probability distribution of source positions in spherical regions containing the sound sources. Here, a weight obtained by analytically solving the spherical integral with the probability distribution as Gaussian is proposed. Schemes of order truncation and hyperparameter optimization for the kernel function are also proposed. Compared with conventional methods, numerical experiments reveal that the proposed method achieves higher sound field estimation accuracy. In addition, Gaussian process regression, using the kernel function with the proposed weight, achieves higher estimation accuracy with lower computational cost than those using the kernel functions with other weights. Moreover, the advantages of the proposed method, which are obtained by treating the sound source as a distribution rather than a point source, are revealed.

Assessing blending of instruments is important in music performance and perception research, but remains underexplored due to its complex multi-dimensional nature. Despite extensive research on source-level blending, the influence of room acoustics on this process is rarely examined. This study proposes a computational modelling approach to evaluate the perceived overall blending between instruments examining the blending at the source-level and its alteration brought by room acoustics. Three audio stimuli, each showcasing different degrees of source-level blending between two violins, were auralized in 25 simulated room acoustic environments, with expert listeners assessing their overall perceived blending. The correlation analysis of room acoustic parameters revealed that their influence on overall blending is contingent upon source-level blending. A random forest regression model is proposed to predict perceived overall blending ratings using source-level blending ratings and room acoustic parameters. Its viability was confirmed through twofold evaluation, including Leave-one-out-cross-validation and separate training and test data, with a mean absolute error of 6% in each case. Feature importance analysis revealed that source-level blending contributes 60%, while room acoustics contribute 40% of the overall perceived blending ratings, with perceived reverberance being the primary contributor. Overall, this investigation contributes to a more holistic understanding of blending perception.

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

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

For improving passive detection of underwater broadband sources, a source-detection and direction-of-arrival-estimation method is developed herein based on a deep neural network (DNN) using a spherical array. Spherical Fourier transform is employed to convert the element pressure signals into spherical Fourier coefficients, which are used as inputs of the DNN. A Gaussian distribution with a spatial-spectrum-like form is adopted to design labels for the DNN. A physical model coupling underwater acoustic propagation and the spherical array is established to simulate array signals for DNN training. The introduction of white noise into the training data considerably enhances the detection capability of the DNN and effectively suppresses false estimation. The model's performance is evaluated based on its detection rate at a constant false alarm rate. Notably, the model does not rely on prior knowledge of the source's spectral features. Further, this study demonstrates that a DNN trained by one source can achieve multisource detection to a certain extent. The simulation and experimental processing results validate the broadband detection capability of the proposed method at varying signal-to-noise ratios.

Microbubbles are of great interest both for ultrasound imaging and for ultrasound-assisted therapy due to their nonlinear scattering, which is enhanced by the viscoelastic shell. A full characterization of this nonlinear response is therefore crucial to fully exploit their potential. Current microbubble characterization techniques rely on assumptions regarding the microbubble shell rheology. Here, a stress-strain method is proposed to characterize the viscoelastic shells of single microbubbles with minimal underlying assumptions, which mainly entail separable viscous and elastic contributions. Detailed knowledge of the acoustic driving pressure and frequency, combined with a precise measurement of the bubble oscillations obtained through high-frequency ultrasound scattering, allows to derive the viscoelastic contribution of single microbubbles. To account for experimental uncertainties, we employed a fitting procedure of the surface tension in the buckled and ruptured regimes, which currently limits the applicability of the method to phospholipid-shelled microbubbles. The method was validated through simulations, and used to experimentally characterize 275 individual microbubbles from a monodisperse population, revealing a shell elasticity of (0.49 ± 0.10) N m-1, and initial surface tension of (28.7±3.94) mN m-1. Besides providing detailed information on single bubble dynamics, this analysis paves the way for the characterization of the viscous dissipation mechanisms of individual microbubble shells.

Bubble curtains are widely used to protect marine life from exposure to harmful noise during offshore pile driving. However, operating a bubble curtain is costly and compliance with government noise regulations remains a challenge. It is therefore important to optimise the acoustic effect of the available compressed air. An interesting approach to achieving this is to split the air flow rate into two separate bubble curtains, rather than one single bubble curtain. This concept is tested both experimentally and numerically in this paper. The experiments and the model show the expected increase in performance of the supplied compressed air when it is split between two manifolds. An increased insertion loss of up to 11 dB is measured. This increase in performance is possibly due to the fact that the reflective properties of the bubble curtains are maintained even when the air flow rate is halved. In effect, by splitting the air flow between two manifolds, a second acoustic barrier is added. Additionally, the variations in the bubble curtain performance between individual measurements are shown to be largely caused by temporal variations in the air distribution within the curtain. The applicability of equivalent fluid models for bubble curtains is discussed, and it is shown that accounting for a gap in the bubble curtain, close to the manifold where the bubble curtain is not yet fully developed, results in better agreement between the modelled and the measured values of the insertion loss.

The speech reception threshold (SRT) model of Plomp [J. Acoust. Soc. Am. 63(2), 533-549 (1978)] can be used to describe SRT (dB signal-to-noise ratio) for 50% of sentences correct in stationary noise in normal-hearing (NH) and hearing-impaired (HI) listeners. The extended speech reception threshold model (ESRT) [Rhebergen et al., J. Acoust. Soc. Am. 117, 2181-2192 (2010)] was introduced to describe the SRT in non-stationary noises. With the ESRT model, they showed that the SRT in non-stationary noises is, contra to the SRT in stationary noise, dependent on the non-stationary noise type and noise level. We examine with SRT data from the literature, whether the ESRT model can also be used to predict SRT in individual NH and HI listeners in different types of non-stationary noise based on a single SRT measurement in quiet, stationary, and non-stationary noise. The predicted speech reception thresholds (SRTs) in non-stationary noises in NH and HI listeners correspond well with the observed SRTs independent of the used non-stationary spectral or temporal masking, or noise masking levels. The ESRT model cannot only be used to describe the SRT within a non-stationary noise but can also be used to predict the SRTs in other non-stationary noise types as a function of noise level in NH and HI listeners.

Designing the soundboards of guitars based on an acoustical and structural approach would ideally allow for the realization of instruments with reproducible acoustical properties and structural stability. This task is challenging because wood, the most common material used for this purpose, is a natural material with variable properties and building instruments using strict geometrical tolerances alone does not ensure reproducible results. Several approaches have been developed so far, some based on tradition and, more recently, on measurement of material properties and computer optimization. In this article, some approaches used to design classical guitar soundboards are reviewed and evaluated. An original builder-friendly method, based on simple definitions of mass and stiffness, is also considered. Finite element analysis is used to evaluate their robustness against variability in wood density and orthotropic stiffness by using the experimentally measured properties of 29 spruce specimens. The results are assessed by comparing the coefficient of variation of acoustically relevant parameters (eigenmodes, eigenfrequencies, mass, and monopole mobility) as well as structurally significant ones (mechanical stiffness of the soundboard and bridge rotation angle). Additionally, the correlation between sound radiation coefficient and monopole mobility is examined. Finally, the practical applicability of these methods is evaluated and discussed.

This study investigates the use of amplitude panning in a localization accuracy test and the influence of a non-ideal environment on its feasibility as a clinical tool. The horizontal localization accuracy of 16 normal-hearing participants and ten bilateral hearing aid users was assessed for real and amplitude panned sound sources produced over loudspeakers. Localization accuracy was measured with speech-shaped noise in both an anechoic chamber (free-field) and an acoustically treated listening room (non-free-field). The root mean square error between the response angle and the target angle was calculated for each participant. Thus, the root mean square error for the two sound source types for each test environment could be calculated and compared, and also contrasted against existing literature. Statistical analysis of the control group results revealed an effect of the target angle, method used (real vs amplitude panning) and environment (free-field vs non-free-field). An interaction between target angle and environment was also found. For the hearing aid user group, however, only an effect of target angle was found, which may lend support to simpler setups with fewer loudspeakers in non-free-field environments. However, the effect of the room varied between individuals within this group, thereby warranting further exploration.