Journal of the Acoustical Society of America最新文献

A difference in fundamental frequency (ΔF0) between two voices provides considerable help for listeners to segregate one from the other perceptually. Past studies have found that the ΔF0 benefit relies on partials of low rank. Here, we questioned whether this observation could partly be due to spectral glimpsing. Four experiments measured speech reception thresholds for a target voice against an interfering voice (experiments 1 and 2) or against buzzes (experiments 3 and 4) with flattened F0 patterns. In experiments 1 and 3, the target F0 was fixed (at 90 Hz) and the masker F0 was raised. In experiments 2 and 4, the masker F0 was fixed (at 90 Hz) and the target F0 was raised. Furthermore, the ΔF0 was (1) applied uniformly across the spectrum, (2) only available in low frequencies, (3) only available in high frequencies, or (4) swapped between low and high frequencies. Results confirmed that glimpsing plays an increasing role when the masker F0 is increased. The benefit relied primarily on frequencies below 800 Hz, but this depended somewhat on the design and on the masker type. High-frequency harmonics contributed more with buzzes, and F0 inconsistencies across channels were more detrimental at large ΔF0s.

Ecoacoustic indices have become widely used tools for monitoring and characterizing soundscapes. Although they have proven useful for relating acoustic measurements to ecological patterns, their conceptual and methodological foundations remain debated. Here, this Letter identifies five recurring limitations, ranging from theoretical disconnects to methodological shortcomings, that undermine their interpretability, reproducibility, and ecological relevance. The Letter concludes by outlining a possible roadmap to address these limitations, including the adoption of auditory-inspired representations, the standardization of computational procedures, validation across contexts, and the use of more explainable modeling approaches.

High-frequency scattering from elastic objects in water can include significant contributions from structural guided waves. Those contributions can appear to be radiated from virtual sources within the structure. Quasi-holographic backpropagation (QHBP) limited to supersonic wave numbers can reveal the location in space and time of the virtual sources of scattered sound [Baik, Dudley, and Marston, J. Acoust. Soc. Am. 130, 3838-3851 (2011)]. Using this technique, the scatter signals of interest can be isolated, eliminating tank reflections and other anomalies. In the present research, the use of QHBP is emphasized to determine information about the backscattered signal, including evidence of axial focusing associated with glory scattering by axisymmetric objects. The location and diameter of virtual ring sources of the glory scattering are revealed. In a bistatic underwater experiment, illuminating an elastic object with a short pulse gives raw data in the form of a space-time plot. From this raw data image, it is shown how backpropagating data is used to obtain information about such features for elastic objects varying in size, shape, and density.

Bayesian seabed geoacoustic inverse problems are commonly addressed using Markov chain Monte Carlo (McMC) methods or their variants, which are computationally expensive. This study proposes an efficient Bayesian geoacoustic inversion approach based on mixture density networks (MDNs). Rather than training separate networks for each parameter, this approach models the joint posterior probability distribution of all parameters. Moreover, the classical Bayesian geoacoustic inversion framework is enhanced by utilizing the MDN theory to analytically deduce essential geoacoustic statistics from the multidimensional posterior probability density (PPD). This approach not only eliminates the computationally intensive numerical integration typically required for Bayesian inversion but also provides deeper insight into the statistical characteristics of the Bayesian PPD. Comparisons of MDN and McMC inversion results across various cases reveal that the inversion using MDN (MDN inversion) produces PPD approximations that align well with the McMC method in terms of overall trends, despite some local discrepancies. Analysis of inter-parameter correlations indicates that the MDN can capture key trade-offs between parameters. This approach offers an efficient method for solving seabed geoacoustic inverse problems within the Bayesian inference framework, making it a promising technique for real-time inversion.

Loud transient signals in underwater acoustic data increase the bias and variance of background noise power spectral density (PSD) estimates based on sample mean. Recently, two PSD estimators mitigated the loud transient impact on PSD estimates by applying order statistics filtering (OSF). The first, the Schwock and Abadi Welch Percentile, scales a single rank order statistic (OS) of consecutive periodograms. The second, the truncated linear order statistics filter, is a weighted sum of OS up to a chosen rank. In order to minimize variance, both classes of OSFs must carefully choose the highest rank that still eliminates the loud transients. However, in real-time applications in dynamic environments, loud transients occur at unpredictable rates, requiring dynamic adjustment of the OSF ranks to keep low bias and variance. To circumvent the challenges of real-time rank selection, this paper proposes a convex sum of OSF ranks with blending weights that are sequentially adjusted to favor the lowest variance OSF ranks over a recent time window. The performance of the blended sum provably approaches the performance of the best fixed-rank OSF. Simulations and real data confirm the blended OSFs effectively filter loud transients out of spectrograms without explicitly choosing a threshold rank.

A graph neural network (GNN) framework is presented for reconstructing room-acoustic sound fields from sparse microphone measurements. Microphones, sources, and candidate field points are represented as a graph whose node and edge embeddings encode geometric priors and physics-aware features related to wave propagation. A principal neighbourhood aggregation architecture performs message passing and readout to estimate complex acoustic pressure at unobserved locations. Experiments on the MeshRIR dataset demonstrate robust reconstruction across a wide range of sampling sparsities and frequencies. Compared with cylindrical harmonics and plane wave expansion with regularized least squares, the proposed GNN yields consistently lower reconstruction error and higher spatial correlation, with gains most evident under very sparse sampling and at higher frequencies. These results indicate that graph-based learning, equipped with geometric and physics-aware representations, provides an effective and physically consistent approach to sound-field reconstruction for room acoustics.

In the biomedical field, flexible acoustic manipulations of high-flux particles play a key role in integrated biomedical detections. Therefore, it is important to propose a method to realize a large-area waveguide with tunable energy concentration and amplification. In this work, by using coupled effect of stepped width of large-area pseudo-spin topological waveguide and rainbow trapping, a tunable energy concentrator is obtained. The results show that the coupled system can be used to tune energy concentration and amplification flexibly, which can amplify the acoustic pressure by 58 times. Moreover, due to the system's tunability, the position of energy concentration and amplification can also be adjusted conveniently. The results present an effective method to tune acoustic energy-intensity amplifications, which is vital in the application fields of acoustic particle manipulation and energy localization.

To address the difficulties in reconstructing transient acoustic fields within a moving medium, an improved convective time-domain equivalent source method is proposed. This method is formulated based on the discretized integral solutions of the scalar and vector convective time-domain Ffowcs Williams-Hawkings equations utilizing monopole source terms. By matching acoustic pressure and particle velocity values at collocation points on an unenclosed sampling surface to the temporal derivatives of equivalent source strengths, a transfer matrix is constructed at each sampling time to progressively determine the unknown source strengths. The joint input of acoustic pressure and three Cartesian components of particle velocity significantly enhances reconstruction accuracy and reduces the number of required sampling points compared to pressure-only methods. Furthermore, a second-order central difference scheme combined with Lagrange linear interpolation is employed to accurately characterize the relationship between source strengths and their temporal derivatives. Additionally, the unphysical assumption that equivalent source strengths vanish prior to the initial source time is removed to capture historical contributions. The method's effectiveness and robustness are validated through numerical test cases in a uniform flow, including steady monopole and dipole sources, and an unsteady composite source.

A three-dimensional, microsphere array, locally resonant phononic crystal has been designed to address the problem of low-frequency vibration in the range of 50-350 Hz in engineering applications. The energy band and transmission loss are calculated using the finite element method. The bandgap formation mechanism is analyzed by co-constructing the mass-spring model and displacement mode. In addition, the effects of material composition, geometric parameters, asymmetry of the connections, and the coating layer containing periodic holes on the bandgap are studied. The results show that as the diameter of the connections decreased, the aperture of the coating layer reduced, and the asymmetry of the connections enhanced, the bandgap of the three-component phononic crystal broadened. When the diameter of the connections is 1 mm, this work achieves the widest mid-gap ratio of 182.76%. Compared to existing models, it has a wider mid-gap ratio. The research results reveal the suppression mechanism and quantify the effectiveness of the proposed metastructure, thereby providing a concrete strategy for attenuating elastic wave propagation within the 50-350 Hz range.

This paper presents a version of an optimization-based model of speech production that reproduces key acoustic and articulatory features of sensorimotor adaptation to altered sensory feedback. In the presented approach, the mechanism of sensorimotor adaptation is solely driven by independently motivated regular updates, based on the sensory feedback perceived by the speaker, of two of the speakers' internal models used for computing (near)-optimal articulation. These internal models predict acoustic and somatosensory consequences of articulatory states, respectively. The paper presents simulations that replicate real adaptation experiments. These show successful reproduction of known aspects of sensorimotor adaptation, including gradual and incomplete adaptation when auditory feedback is suddenly altered (F1-shifted), adaptation to non-uniform auditory perturbations, and generalization of adaptation behavior to untrained vowels. They also show that the rate and magnitude of adaptation behavior depend on a small number of parameters in the proposed model. Variation in the values of these parameters explains inter-speaker differences in terms of adaptation behavior, including sensory preference.