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Related Concept Videos

Auditory Perception01:17

Auditory Perception

998
The auditory system is essential for sound perception, utilizing various critical structures. When sound waves enter the outer ear, they travel through the ear canal and cause the eardrum to vibrate. These vibrations are then transmitted to the middle ear, where three tiny bones – the malleus, incus, and stapes – amplify the sound. This amplification is crucial, as it ensures that the sound vibrations are strong enough to be conveyed to the inner ear. These vibrations then reach the...
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Hearing01:31

Hearing

56.3K
When we hear a sound, our nervous system is detecting sound waves—pressure waves of mechanical energy traveling through a medium. The frequency of the wave is perceived as pitch, while the amplitude is perceived as loudness.
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Related Experiment Video

Updated: Jan 10, 2026

Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages
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Can Information Representations Inspired by the Human Auditory Perception Benefit Computer Audition-Based Disease

Zhihua Wang, Yang Tan, Haojie Zhang

    IEEE Journal of Biomedical and Health Informatics
    |November 28, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Auditory perception-inspired features significantly enhance computer-aided disease detection accuracy. Gammatone Frequency Cepstral Coefficients (GFCCs) show superior performance, especially in noisy environments, due to robust feature capture.

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    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Artificial Intelligence

    Background:

    • Computer audition offers non-invasive disease detection.
    • Human auditory perception-inspired features show promise but require thorough investigation.
    • Understanding performance differences in these features is crucial for advancing the field.

    Purpose of the Study:

    • To conduct an interpretable comparative study of auditory perception-inspired information representations for disease detection.
    • To investigate the impact of these representations on detection accuracy and noise robustness.
    • To analyze the underlying reasons for performance variations using explainable AI.

    Main Methods:

    • Evaluated various information representations on psychological and physiological disease sound datasets.
    • Employed a classical model and a proposed Temporal-Spatial Multi-Scale Perception Network.
    • Assessed noise robustness using Gaussian noise at different signal-to-noise ratios (SNRs).
    • Utilized explainable AI techniques to analyze performance differences.

    Main Results:

    • Auditory perception-inspired representations significantly improve disease detection performance.
    • Gammatone Frequency Cepstral Coefficients (GFCCs) achieved the highest accuracy, outperforming other representations.
    • GFCCs demonstrated superior noise robustness, maintaining high performance under varying SNRs.
    • Interpretable analysis revealed GFCCs' effectiveness in capturing critical auditory features.

    Conclusions:

    • Information representations inspired by human auditory perception are effective for computer-aided disease detection.
    • GFCCs represent a robust and high-performing feature extraction method for this application, particularly in challenging acoustic conditions.
    • This study provides a foundation for developing more advanced and interpretable computer audition systems for disease diagnosis.