Urban soundscape design involves creating outdoor spaces that are pleasing to the ear. One way to achieve this goal is to add or accentuate sounds that are considered to be desired by most users of the space, such that the desired sounds mask undesired sounds, or at least distract attention away from undesired sounds. In view of removing the need for a listening panel to assess the effectiveness of such soundscape measures, the interest for new models and techniques is growing. In this paper, a model of auditory attention to environmental sound is presented, which balances computational complexity and biological plausibility. Once the model is trained for a particular location, it classifies the sounds that are present in the soundscape and simulates how a typical listener would switch attention over time between different sounds. The model provides an acoustic summary, giving the soundscape designer a quick overview of the typical sounds at a particular location, and allows assessment of the perceptual effect of introducing additional sounds.
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http://dx.doi.org/10.1121/1.4807798 | DOI Listing |
J Voice
January 2025
Division of Phoniatrics and Pediatric Audiology at the Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany.
Objectives: This study investigates the use of sustained phonations recorded during high-speed videoendoscopy (HSV) for machine learning-based assessment of hoarseness severity (H). The performance of this approach is compared with conventional recordings obtained during voice therapy to evaluate key differences and limitations of HSV-derived acoustic recordings.
Methods: A database of 617 voice recordings with a duration of 250 ms was gathered during HSV examination (HS).
Braz J Otorhinolaryngol
January 2025
Ankara Yıldırım Beyazıt University Faculty of Medicine, Department of Otorhinolaryngology, Ankara, Turkey.
Objectives: The aim of this study was to investigate the risk factors that may cause postoperative otomycosis in patients undergoing Chronic Nonsuppurative Otitis Media (CNSOM) surgery.
Methods: In this retrospective study, 409 out of 523 patients met the inclusion criteria. 44 patients diagnosed with otomycosis CNSOM were analyzed.
J Morphol
January 2025
Department of Zoology, Denver Museum of Nature & Science, Denver, Colorado, USA.
The barn owl is a common research subject in auditory science due to its exceptional capacity for high frequency hearing and superb sound source localization capabilities. Despite longstanding interest in the auditory performance of barn owls, the function of its middle ear has attracted remarkably little attention. Here, we report the middle ear transfer function measured by laser Doppler vibrometry and direct measurements of inner ear pressures.
View Article and Find Full Text PDFJ Neurodev Disord
January 2025
Graduate Neuroscience Program, University of California, Riverside, CA, USA.
Background: Fragile X syndrome (FXS) is a leading known genetic cause of intellectual disability and autism spectrum disorders (ASD)-associated behaviors. A consistent and debilitating phenotype of FXS is auditory hypersensitivity that may lead to delayed language and high anxiety. Consistent with findings in FXS human studies, the mouse model of FXS, the Fmr1 knock out (KO) mouse, shows auditory hypersensitivity and temporal processing deficits.
View Article and Find Full Text PDFeNeuro
January 2025
Neurophysiology of Everyday Life Group, Department of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
A comprehensive analysis of everyday sound perception can be achieved using Electroencephalography (EEG) with the concurrent acquisition of information about the environment. While extensive research has been dedicated to speech perception, the complexities of auditory perception within everyday environments, specifically the types of information and the key features to extract, remain less explored. Our study aims to systematically investigate the relevance of different feature categories: discrete sound-identity markers, general cognitive state information, and acoustic representations, including discrete sound onset, the envelope, and mel-spectrogram.
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