3 accepted papers
We propose a brain-informed speech separation method for cochlear implants (CIs) that uses electroencephalography (EEG)-derived attention cues to guide enhancement toward the attended speaker. An attention-guided network fuses audio mixtures with EEG features through a lightweight fusion layer, prod…
This paper investigates an end-to-end speech signal denoising approach for cochlear implants (CIs). Building on previous work, we first explore the effect of relocating the deep envelope detector within the deep learning-based CI sound coding strategy, moving it from the skip connection to the outpu…
Cochlear implant (CI) users struggle to understand speech in noisy conditions. To address this problem, we propose a deep learning speech denoising sound coding strategy that estimates the CI electric stimulation patterns out of the raw audio data captured by the microphone, performing end-to-end CI…