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Updated: Jan 13, 2026

Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages
Published on: March 24, 2023
End-to-end audio-visual learning for cochlear implant sound coding simulations in noisy environments
Meng-Ping Lin1, Enoch Hsin-Ho Huang2, Shao-Yi Chien1
1Graduate Institute of Electronics Engineering and the Department of Electrical Engineering, National Taiwan University, Taipei 106319, Taiwan.
Abstract:
The cochlear implant (CI) is a successful biomedical device that enables individuals with severe-to-profound hearing loss to perceive sound through electrical stimulation, yet listening in noise remains challenging. Recent deep learning advances offer promising potential for CI sound coding by integrating visual cues. In this study, an audio-visual speech enhancement (AVSE) module is integrated with the ElectrodeNet-CS (ECS) model to form the end-to-end CI system, AVSE-ECS. Simulations show that the AVSE-ECS system with joint training achieves high objective speech intelligibility and improves the signal-to-error ratio by 7.4666 dB compared to the advanced combination encoder strategy. These findings underscore the potential of AVSE-based CI sound coding.
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