JASA express letters最新文献

A recent study in individuals with hyperfunctional voice disorders (HVDs) reported poorer fo discrimination compared to matched controls. In that study, fo discrimination was measured using each individual's own voice as the stimulus, which may have introduced confounds given the differences in vocal quality between the groups. Here, this paper addressed this possibility by using an fo discrimination task where participants with and without HVDs judged the same external voice. In line with the previous study, the HVD group exhibited poorer fo discrimination than controls, providing convergent evidence for a pitch-related auditory deficit in this population.

Degraded speech intelligibility in multitalker situations can be improved by spatial unmasking, using sound spatialization techniques such as binaural synthesis. However, intelligibility also depends on the ability to focus efficiently on the target. Three experimental sessions explored the benefit of an additional vibrotactile cue, spatialized around the waist, on intelligibility in different spatialized multitalker situations. Results indicate improvements in intelligibility scores and reduced listening effort specifically for an off-center target among masker talkers. Multimodality allows us to better understand the mechanisms of auditory attention and to open up new perspectives for improving speech intelligibility in multitalker situations.

The complexity of deep-sea acoustic ray bending is positively correlated with propagation distance, typically turning beyond 10 km. Existing ranging correction methods fail under turning conditions, limiting ranging accuracy. This letter proposes an effective sound velocity estimation method for turning ray, based on reliable acoustic path boundary to classify turning direct sound and establish a ray-tracing model for ranging correction. This method extends high-accuracy ranging from a 10 km scale to the first convergence zone. Experimental results demonstrate that the method achieves a positioning precision of 6.18 m at a horizontal distance of 34 km, validating its effectiveness.

This study presents a probabilistic one-step two-dimensional (2D) inversion method of spherical-wave reflection coefficient data to estimate the range-dependent structure and geoacoustic parameters along a track. The approach of inverting such datasets independently as one-dimensional (1D) layered models and merging them to a 2D section is feasible but computationally expensive. This study demonstrates a more parsimonious 2D parametrization for active source data recorded on a towed hydrophone array. The comparison of data variance reduction for 1D- and 2D-based results clearly favors the 2D parametrization described here.

Trans-dimensional (trans-D) Bayesian inversion is a powerful approach to estimate seabed geoacoustic models from ocean-acoustic data, combining quantitative model selection and uncertainty estimation. Trans-D inversion samples probabilistically over the number of seabed layers and the geoacoustic parameters for each layer, with layers added and removed in sampling, changing the dimension of the model. However, the probability of accepting dimension changes can approach zero for problems involving highly informative data or large numbers of parameters per layer. This Letter examines the use of parallel tempering, which employs a sequence of interacting Markov chains with successively relaxed likelihoods, to address these challenging cases.

In this study, the question of generalization for dimension-based statistical learning in speech perception is revisited. Learning for F0 and voice-onset-time as cues to stop voicing has been suggested to be fairly specific to a particular contrast, which was previously shown not to generalize between two places of articulation. The present study seeks to replicate generalization for the same place of articulation, using more varied stimuli and a different design. Then, it is tested if increased evidence for a distributional pattern, i.e., two places of articulation showing that pattern, leads to generalization to a third place of articulation. Same place of articulation learning is replicated, and no generalization across place of articulation is found, reaffirming that learning appears to be quite specific.

The size of an individual's tongue relative to the oral cavity is associated with articulation speed [Feng, Lu, Zheng, Chi, and Honda, in Proceedings of the 10th Biennial Asia Pacific Conference on Speech, Language, and Hearing (2017), pp. 17-19)] and may affect speech clarity. This study introduces an ultrasound-based method for measuring relative tongue size, termed ultrasound-based relative tongue size (uRTS), as a cost-effective alternative to the magnetic resonance imaging (MRI) based method. Using deep learning to extract the tongue contour, uRTS was calculated from tongue and oropharyngeal cavity sizes in the midsagittal plane. Results from ten speakers showed a strong correlation between uRTS and MRI-based measurements (r = 0.87) and a negative correlation with tongue movement speed (r = -0.73), indicating uRTS is a useful index for assessing tongue size.

This study enhances the approximation "radiation efficiency varying equivalent radiated power." This is done through introducing a new method for estimating radiation efficiencies, based on spherical harmonic decomposition. The proposed improvements eliminate the need for computationally expensive surface integrals and results in solution times comparable with the classical equivalent radiated power approximation. This is achieved while significantly outperforming classical equivalent radiated power in terms of accuracy when compared with full numerical solutions to Helmholtz equation. This is shown both quantitively and qualitatively through numerical acoustic models of two systems of industrial complexity. The proposed improvements make the method robust for non-convex geometries across varying mesh densities, making the method highly suitable for iterative acoustic analysis in industrial applications.

Objects in synthetic aperture sonar imagery that lie proud above an interface will occlude a portion of the background behind it. This occlusion may appear as a shadow in the imagery whose dimensions can be used to infer the height of the object. Volume scattering from beneath the object, however, may prevent such shadows from being visible in imagery. Regions of occlusion can be detected using the coherence between two images collected from different horizontal baselines. Using controlled laboratory experimentation, this approach is shown to detect occluded regions for accurate estimation of the height of an object.

The implicit representation by physics-informed neural networks (PINNs) serves as an effective solution for a key challenge faced by optical sound measurements. Since optical sound measurements observe line integral of the sound pressure along the optical path, reconstruction is necessary to determine the sound pressure at each point in the three-dimensional field. In this paper, we expand the PINNs-based reconstruction method into three-dimensional reconstruction and demonstrate its effectiveness for optically measured sound fields. Furthermore, we propose a reconstruction approach which can estimate solutions well outside the bounds of the data used for training.