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Related Concept Videos

Perceiving Loudness, Pitch, and Location01:21

Perceiving Loudness, Pitch, and Location

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The human brain perceives pitch through two primary mechanisms reflected in place theory and frequency theory. Each mechanism describes how sound waves are interpreted as specific pitches by the brain, offering insights into the intricate processes of auditory perception.
Place theory, or place coding, suggests that different pitches are heard because various sound waves activate specific locations along the cochlea's basilar membrane. The brain determines the pitch of a sound by...
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The Cochlea01:13

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The cochlea is a coiled structure in the inner ear that contains hair cells—the sensory receptors of the auditory system. Sound waves are transmitted to the cochlea by small bones attached to the eardrum called the ossicles, which vibrate the oval window that leads to the inner ear. This causes fluid in the chambers of the cochlea to move, vibrating the basilar membrane.
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Hearing

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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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A Supervised Speech Enhancement Method for Smartphone-Based Binaural Hearing Aids.

Zhuoyi Sun, Yingdan Li, Hanjun Jiang

    IEEE Transactions on Biomedical Circuits and Systems
    |April 21, 2020
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    This study introduces a new real-time speech enhancement method for hearing aids using recurrent neural networks (RNNs). The approach improves speech intelligibility in noisy environments, enhancing user experience.

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    Area of Science:

    • Audiology and Speech-Language Pathology
    • Artificial Intelligence
    • Signal Processing

    Background:

    • Hearing aid development faces challenges in preserving speech quality and clarity.
    • Deep learning methods show promise for speech enhancement but often lack real-time capabilities crucial for hearing aids.
    • Existing algorithms struggle with real-time processing, particularly in smartphone-integrated binaural hearing aid systems.

    Purpose of the Study:

    • To develop a supervised speech enhancement method that achieves real-time processing for hearing aids.
    • To address the challenge of speech information loss and distortion in hearing aid algorithms.
    • To improve speech intelligibility in low signal-to-noise ratio (SNR) environments.

    Main Methods:

    • A supervised speech enhancement technique utilizing a Recurrent Neural Network (RNN) architecture.
    • Framing the problem as a resource-constrained speech intelligibility improvement task.
    • Evaluating the method using standard objective metrics and user trials.

    Main Results:

    • The proposed RNN-based method demonstrates superior performance in objective speech enhancement evaluations.
    • Experimental results confirm the effectiveness of the algorithm in improving speech intelligibility.
    • User trials indicated a significant improvement in the overall user experience.

    Conclusions:

    • The developed RNN-based speech enhancement method effectively addresses the real-time processing limitations of current hearing aid technologies.
    • The approach successfully enhances speech intelligibility in challenging low SNR conditions.
    • The method offers a promising solution for improving the user experience in modern hearing aid systems.