Physiological studies of spatial hearing show that the spatial receptive fields of cortical neurons typically are narrow at near-threshold levels, broadening at moderate levels. The apparent loss of neuronal spatial selectivity at increasing sound levels conflicts with the accurate performance of human subjects localizing at moderate sound levels. In the present study, human sound localization was evaluated across a wide range of sensation levels, extending down to the detection threshold. Listeners reported whether they heard each target sound and, if the target was audible, turned their heads to face the apparent source direction. Head orientation was tracked electromagnetically. At near-threshold levels, the lateral (left/right) components of responses were highly variable and slightly biased towards the midline, and front vertical components consistently exhibited a strong bias towards the horizontal plane. Stimulus levels were specified relative to the detection threshold for a front-positioned source, so low-level rear targets often were inaudible. As the sound level increased, first lateral and then vertical localization neared asymptotic levels. The improvement of localization over a range of increasing levels, in which neural spatial receptive fields presumably are broadening, indicates that sound localization does not depend on narrow spatial receptive fields of cortical neurons.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.heares.2004.08.001 | DOI Listing |
Physiol Meas
January 2025
Nanchang University, 1st Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, 330031, CHINA.
Background And Objective: In contrast to respiratory sound classification, respiratory phase and adventitious sound event detection provides more detailed and accurate respiratory information, which is clinically important for respiratory disorders. However, current respiratory sound event detection models mainly use convolutional neural networks to generate frame-level predictions. A significant drawback of the frame-based model lies in its pursuit of optimal frame-level predictions rather than the best event-level ones.
View Article and Find Full Text PDFJASA Express Lett
January 2025
School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China.
This letter proposed a sparse deconvolution localization method (FFT-L1ML2) driven by non-convex L1-αL2 regularization that more closely approximates the ideal L0 norm. It is an alternative that explores the sparse structure of sound sources to enhance localization accuracy, while the original sparse deconvolution beamforming lacks a sufficiently accurate sparse description. An optimization solver composed of forward gradient descent and backward proximal operator is then developed for the FFT-L1ML2 model to reconstruct the beamforming map.
View Article and Find Full Text PDFJ Acoust Soc Am
January 2025
Dyson School of Design Engineering, Imperial College London, SW7 2DB London, United Kingdom.
To date, there is strong evidence indicating that humans with normal hearing can adapt to non-individual head-related transfer functions (HRTFs). However, less attention has been given to studying the generalization of this adaptation to untrained conditions. This study investigated how adaptation to one set of HRTFs can generalize to another set of HRTFs.
View Article and Find Full Text PDFPsychol Res
January 2025
Center for Mind/Brain Sciences (CIMeC), University of Trento, Rovereto, TN, Italy.
Each perceptual process is accompanied with an evaluation regarding the reliability of what we are perceiving. The close connection between confidence in perceptual judgments and planning of actions has been documented in studies investigating visual perception. Here, we extend this investigation to auditory perception by focusing on spatial hearing, in which the interpretation of auditory cues can often present uncertainties.
View Article and Find Full Text PDFISA Trans
December 2024
Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK. Electronic address:
As artificial intelligence advances and demand for cost-effective equipment maintenance in various fields increases, it is worth insightful research on utilizing robots embedded with sound source localization (SSL) technology for condition monitoring. Combining the two techniques has significant advantages, which are conducive to further classifying and tracking abnormal sources, thereby enhancing system performance at a lower cost. The paper provides an overview of current acoustic-based robotic techniques for condition monitoring, highlights the common SSL methods, and finds that localization performance heavily depends on signal quality.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!