4 accepted papers
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…
Electroencephalography (EEG) data can be used to decode an attended speech source in normal-hearing (NH) listeners. One application of this technology consists of identifying the target speaker in a cocktail party-like scenario and activate speech enhancement algorithms in cochlear implants (CIs). I…
Speech intelligibility in noisy environments is still quite limited for cochlear implant (CI) users. Classical beamformers such as the Generalized Sidelobe Canceller (GSC) can provide large improvements in speech intelligibility for CI users. These algorithms have been adopted from hearing aids and…