JASA express letters最新文献

This article introduces an analytical framework for modeling head-related transfer functions (HRTFs) from a listener-centered perspective. The distinction between strong (or general) HRTFs, aiming for idealized physical acoustic fidelity, and weak (or narrow) HRTFs, prioritizing perceptual adequacy in task-specific contexts, frames the contrast in multiple contrasting definitions and scientific methodologies by drawing inspiration from the debate in artificial intelligence. The proposed formalism adopts a Bayesian structure that models HRTFs through a state-space formulation capturing anatomical, contextual, experiential, and task-related factors: the eHRTF. The "e" emphasizes the egocentric perspective, transforming HRTFs from static measurements into mutable auditory representations continuously updated through the listener's feedback. Satisfaction regions are defined in probabilistic terms and characterize how different classes of HRTFs, i.e., individual, generic, super, and personalized, meet perceptual requirements under varying tasks and their complexity.

This study examined the effects of age and sentence context on perceiving speech temporal cues in noise. Adults 18 to 81 yrs of age with self-reported normal hearing performed a phonemic categorization task using the dent/tent contrast with varied voice-onset times (VOTs). Target words were embedded in sentences that biased perception toward "dent," "tent," or provided no bias, presented in speech-shaped noise at a 0 dB signal-to-noise ratio. Results showed reduced VOT processing with increasing age in neutral and dent-biasing contexts but not in tent-biasing contexts. Findings suggest that contextual influences can shape age-related changes in temporal processing in challenging listening conditions.

With the aim of achieving strong absorption over a wide range of frequencies and angles of incidence, the reflection of an acoustic wave by a rigidly-backed periodic layer of inclined ultrathin resistive sheet is studied theoretically and numerically. It appears that the structure exhibits either almost omnidirectional large absorption over a subwavelength frequency band for large resistivity of the wiremesh or large absorption over a broad frequency band for medium resistivity. Both optimal values are obtained for large inclination angles. These findings provide promising perspectives for the design of broadband omnidirectional subwavelength sound-absorbing devices.

Marine vessels are mandated or requested to reduce speed to meet operational, economic, and conservation goals. Vessel speed reduction (VSR) is a key strategy in global efforts to reduce ocean noise. Many regional VSR programs incorporate underwater acoustic monitoring to assess reductions in underwater radiated noise (URN) from vessels. Drawing from North American VSR programs, approaches to measuring URN reduction are assessed and progress toward robust metrics, scalable methods, and integrative measures are documented. Improved alignment across programs is recommended across programs to achieve measurement comparability to advance VSR evaluation and contribute to global underwater noise reduction and sustainable shipping goals.

Model-based deep learning approaches provide an alternative scheme to address the problem of the shortage of training data. However, performance degradation caused by sound speed profile (SSP) mismatch remains a critical challenge, particularly in shallow-water environments influenced by internal waves. In this paper, a simple range-dependent SSP model is integrated into the deep learning approach for source localization. The network trained on simulated data generated with the range-dependent SSP model performs well on validation data and generalizes to experimental test data after transfer learning with limited experimental samples.

The presence of unavoidable background noise limits the signal-to-noise ratio in measured room impulse responses (RIRs). A common solution is to crop the RIR to the time interval where the signal dominates the background noise, but finding the correct onset and truncation points is challenging. It usually requires estimating the sound decay rate and noise floor, which is burdened with uncertainty. In this study, we propose an RIR cropping method based on the covariance between two repeated RIRs and its inherent monotonicity. Evaluation on measured RIRs shows the proposed method is highly robust in different scenarios and outperforms state-of-the-art algorithms.

Echolocating big brown bats (Eptesicus fuscus) perceive a target's reflecting parts, or glints, from spectral peaks and nulls in echoes. Glints serve as cues for object size and shape. In this exploratory study, we investigated whether the scalp-recorded auditory brainstem response (ABR) carries information about glints. We show that ABR amplitude, latency, and root mean square energy do not differentiate between glints in a manner paralleling the bat's perception. ABR recordings, even though they encode some perceptually relevant features of echoes, may not be sensitive enough to distinguish these more subtle cues.

The deconvolved beamforming (dCv) improves spatial resolution without expanding the array aperture but fails for the shift-variant beam pattern and the real targets, which are not located on the sampling grids. To solve them, this Letter extends the off-grid sparse Bayesian learning (OGSBL) to dCv because the generalized convolutional model considers the beam pattern at each angle in beam domain. OGSBL reduces modeling errors by parameterizing sampled locations in coarse grids. Controlling the number of output beams from conventional beamforming to cover the spatial area of interest could accelerate convergence without sacrificing accuracy. The simulation results confirm the good performance.

We applied passive acoustic monitoring and Hierarchical Density-Based Spatial Clustering of Applications with Noise clustering to define spatiotemporally cohesive groupings of franciscana dolphin (Pontoporia blainvillei) echolocation click trains. This unsupervised, objective method identified biologically relevant click train clusters, offering rare insights into the species' social organization. The observed structure revealed consistent intra-cluster cohesion and inter-cluster separation, supporting the effectiveness of the approach. Our findings demonstrate that clustering acoustic detections can serve as a robust framework for delineating social groups and can be integrated into future density estimation protocols, enhancing the ecological understanding and conservation potential for this cryptic and vulnerable species.