A sound that we hear in a natural setting allows us to identify the sound source and localize it in space. The two aspects can be disrupted independently as shown in a study of 15 patients with focal right-hemispheric lesions. Four patients were normal in sound recognition but severely impaired in sound localization, whereas three other patients had difficulties in recognizing sounds but localized them well. The lesions involved the inferior parietal and frontal cortices, and the superior temporal gyrus in patients with selective sound localization deficit; and the temporal pole and anterior part of the fusiform, inferior and middle temporal gyri in patients with selective recognition deficit. These results suggest separate cortical processing pathways for auditory recognition and localization.
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http://dx.doi.org/10.1007/s00221-002-1203-9 | 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.
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