JASA express letters最新文献

Large surface wave breaking events in deep water are acoustically detectable by beamforming at 5-6 kHz with a mid-frequency planar array located 130 m below the surface. Due to the array's depth and modest 1 m horizontal aperture, wave breaking events cannot be tracked accurately by beamforming alone. Their trajectories are estimated instead by splitting the array into sub-arrays, beamforming each sub-array toward the source, and computing the temporal cross-correlation of the sub-array beams. Source tracks estimated from sub-array cross-correlations match the trajectories of breaking waves that are visible in aerial images of the ocean surface above the array.

An underwater acoustic recorder was moored off Heard Island from September 2017 through March 2018 to listen for marine mammals. Analysis of data was initially conducted by visual inspection of long-term spectral averages to reveal sounds from sperm whales, Antarctic and pygmy blue whales, fin whales, minke whales, odontocete whistles, and noise from nearby ships. Automated detection of sperm whale clicks revealed they were seldom detected from September through January (n = 35 h) but were detected nearly every day of February and March (n = 684 h). Additional analysis of these detections revealed further diel and demographic patterns.

Passive detection of target-of-interest (TOI) within strong interferences poses a challenge. This paper introduces an adaptive interference suppression based on an invariant subspace of matrix matching. Assume that the TOI-bearing intervals are known. We define a correlation ratio for each eigenvector to obtain the highest one. Then, we use invariant subspace of matrix matching to measure the distance between two invariant projection matrices of this eigenvector. This identifies and removes the eigenvectors associated with TOI. Finally, the remaining eigenvectors are subtracted from the sample covariance matrix to suppress interference and noise. The viability of the proposed method is demonstrated experimentally.

This Letter proposes a low-complexity joint equalization and decoding reception scheme based on super-trellis per-survivor processing, making it possible to apply maximum likelihood sequence estimation in high-order underwater acoustic communications under fast time-varying channels. The technique combines trellis-coded modulation states and intersymbol interference states and uses per-survivor processing to track channel parameters. Furthermore, a general trellis configuration for arbitrary order quadrature amplitude modulation signal is provided when truncate the channel is used to describe the intersymbol interference state to 1. Sea trials results show that the performance of proposed method can be more than 1.4 dB superiority than conventional schemes.

A long-standing quest in audition concerns understanding relations between behavioral measures and neural representations of changes in sound intensity. Here, we examined relations between aspects of intensity perception and central neural responses within the inferior colliculus of unanesthetized rabbits (by averaging the population's spike count/level functions). We found parallels between the population's neural output and: (1) how loudness grows with intensity; (2) how loudness grows with duration; (3) how discrimination of intensity improves with increasing sound level; (4) findings that intensity discrimination does not depend on duration; and (5) findings that duration discrimination is a constant fraction of base duration.

Two measures of auditory spatial resolution, the minimum audible angle and the minimum audible movement angle, have been obtained in a simulated acoustic environment using Ambisonics sound field reproduction. Trajectories were designed to provide no reliable cues for the spatial discrimination task. Larger threshold angles were found in reverberant compared to anechoic conditions, for stimuli on the side compared to the front, and for moving compared to static stimuli. The effect of reverberation appeared to be independent of the position of the sound source (same relative threshold increase) and was independently present for static and moving sound sources.

The perceptual boundary between short and long categories depends on speech rate. We investigated the influence of speech rate on perceptual boundaries for short and long vowel and consonant contrasts by Spanish-English bilingual listeners and English monolinguals. Listeners tended to adapt their perceptual boundaries to speech rates, but the strategy differed between groups, especially for consonants. Understanding the factors that influence auditory processing in this population is essential for developing appropriate assessments of auditory comprehension. These findings have implications for the clinical care of older populations whose ability to rely on spectral and/or temporal information in the auditory signal may decline.

Predicting acoustic transmission loss in the SOFAR channel faces challenges, such as excessively complex algorithms and computationally intensive calculations in classical methods. To address these challenges, a deep learning-based underwater acoustic transmission loss prediction method is proposed. By properly training a U-net-type convolutional neural network, the method can provide an accurate mapping between ray trajectories and the transmission loss over the problem domain. Verifications are performed in a SOFAR channel with Munk's sound speed profile. The results suggest that the method has potential to be used as a fast predicting model without sacrificing accuracy.

Bimodal stimulation, a cochlear implant (CI) in one ear and a hearing aid (HA) in the other, provides highly asymmetrical inputs. To understand how asymmetry affects perception and memory, forward and backward digit spans were measured in nine bimodal listeners. Spans were unchanged from monotic to diotic presentation; there was an average two-digit decrease for dichotic presentation with some extreme cases of decreases to zero spans. Interaurally asymmetrical decreases were not predicted based on the device or better-functioning ear. Therefore, bimodal listeners can demonstrate a strong ear dominance, diminishing memory recall dichotically even when perception was intact monaurally.

Machine learning enabled auscultating diagnosis can provide promising solutions especially for prescreening purposes. The bottleneck for its potential success is that high-quality datasets for training are still scarce. An open auscultation dataset that consists of samples and annotations from patients and healthy individuals is established in this work for the respiratory diagnosis studies with machine learning, which is of both scientific importance and practical potential. A machine learning approach is examined to showcase the use of this new dataset for lung sound classifications with different diseases. The open dataset is available to the public online.