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Non-linguistic sounds (NLSs) are a core feature of our everyday life and many evoke powerful cognitive and emotional outcomes. The subjective perception of NLSs by humans has occasionally been defined for single percepts, e.g., their pleasantness, whereas many NLSs evoke multiple perceptions. There has also been very limited attempt to determine if NLS perceptions are predicted from objective spectro-temporal features. We therefore examined three human perceptions well-established in previous NLS studies ("Complexity," "Pleasantness," and "Familiarity"), and the accuracy of identification, for a large NLS database and related these four measures to objective spectro-temporal NLS features, defined using rigorous mathematical descriptors including stimulus entropic and algorithmic complexity measures, peaks-related measures, fractal dimension estimates, and various spectral measures (mean spectral centroid, power in discrete frequency ranges, harmonicity, spectral flatness, and spectral structure). We mapped the perceptions to the spectro-temporal measures individually and in combinations, using complex multivariate analyses including principal component analyses and agglomerative hierarchical clustering.
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http://dx.doi.org/10.3389/fnins.2019.00794 | DOI Listing |
J Imaging
July 2025
Digital Healthcare Research Center, Pukyong National University, Busan 48513, Republic of Korea.
This study presents a novel framework that integrates Vision Graph Neural Networks (ViGs) with supervised contrastive learning for enhanced spectro-temporal image analysis of speech signals in Parkinson's disease (PD) detection. The approach introduces a frequency band decomposition strategy that transforms raw audio into three complementary spectral representations, capturing distinct PD-specific characteristics across low-frequency (0-2 kHz), mid-frequency (2-6 kHz), and high-frequency (6 kHz+) bands. The framework processes mel multi-band spectro-temporal representations through a ViG architecture that models complex graph-based relationships between spectral and temporal components, trained using a supervised contrastive objective that learns discriminative representations distinguishing PD-affected from healthy speech patterns.
View Article and Find Full Text PDFEar Hear
June 2025
Cambridge Hearing Group, MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.
Objectives: Evaluating adjustments to cochlear implant (CI) settings is challenging as recipients need time to adapt for optimal speech test performance. The Spectro-Temporal Ripple for Investigating Processor EffectivenesS (STRIPES) test, a language-independent measure of spectro-temporal resolution, has been validated with Advanced Bionics and Cochlear CI systems. This study investigates if performance on the STRIPES test varies with presentation level in a loudspeaker setup and its relationship with outcomes on the British Coordinate Response Measure (CRM) test.
View Article and Find Full Text PDFInt J Pediatr Otorhinolaryngol
May 2025
Department of Biostatistics, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran.
Background And Objective: Children with hearing loss often have difficulty understanding speech in noisy environments like classrooms, leading to educational and communication challenges. Detecting and discriminating auditory spectro-temporal fundamentals is essential for speech comprehension. So, in this study, we investigated how children with mild to moderate hearing loss (MMHL) process these auditory modulations and their relation to speech perception in noise, comparing their performance to that of children with normal hearing.
View Article and Find Full Text PDFActa Otolaryngol
October 2024
Senior Department of Otolaryngology Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China.
Background: An objective measurement of speech perception would be valuable in hearing-impaired patients who are unable to perform auditory tasks reliably.
Objectives: To investigate the feasibility of cortical auditory evoked potentials (CAEPs) evoked by different spectro-temporal modulation (STM) signals and provide reference for the further exploration of acoustic change complex (ACC) in hearing-impaired patients.
Method: 29 normal hearing (NH) adults were recruited and stimulated randomly by STM signals at 6 spectral modulation rates: 0, 1, 2, 4, 8, 16 cycles/octave, at each of 4 temporal modulation rates: 0, 2, 4, 8 Hz, to elicit ACC response.
A patient-independent approach for continuous estimation of vital signs using robust spectro-temporal features derived from only photoplethysmogram (PPG) signal. In the pre-processing stage, we remove baseline shifts and artifacts of the PPG signal using Incremental Merge Segmentation with adaptive thresholding. From the cleaned PPG, we extract multiple parameters independent of individual patient PPG morphology for both Respiration Rate (RR) and Blood Pressure (BP).
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