What question did this study set out to answer?

To develop and evaluate an end-to-end audio-visual deep learning framework to enhance cochlear implant sound coding in noisy acoustic environments.

January 1, 2026JASA Express LettersOpen Access

End-to-end audio-visual learning for cochlear implant sound coding simulations in noisy environments

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To develop and evaluate an end-to-end audio-visual deep learning framework to enhance cochlear implant sound coding in noisy acoustic environments.
Integrated an audio-visual speech enhancement module with the ElectrodeNet-CS model to construct the end-to-end AVSE-ECS system.
Trained the integrated network using joint optimization and simulated electrical stimulation patterns across noisy conditions against the advanced combination encoder strategy.
Demonstrated high objective speech intelligibility using the jointly trained AVSE-ECS architecture.
Achieved a 7.4666 dB improvement in signal-to-error ratio over the standard advanced combination encoder strategy.

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Implication

Simulation study demonstrates improved signal-to-error ratio and speech intelligibility using audio-visual deep learning for cochlear implants, suggesting better speech clarity in noisy settings.

Cite This Study

Lin et al. (2026) studied this question.

synapsesocial.com/papers/6a08f96d2757fd3263d392a9 https://doi.org/10.1121/10.0042198

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Implication

Simulation study demonstrates improved signal-to-error ratio and speech intelligibility using audio-visual deep learning for cochlear implants, suggesting better speech clarity in noisy settings.

End-to-end audio-visual learning for cochlear implant sound coding simulations in noisy environments

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Discussion

Implication

Cite This Study

End-to-end audio-visual learning for cochlear implant sound coding simulations in noisy environments

Key Points

Discussion

Implication

Cite This Study

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