  Attempts to develop speech enhancement algorithms with improved speech
intelligibility for cochlear implant (CI) users have met with limited success.
To improve speech enhancement methods for CI users, we propose to perform
speech enhancement in a cochlear filter-bank feature space, a feature-set
specifically designed for CI users based on CI auditory stimuli. We leverage a
convolutional neural network (CNN) to extract both stationary and
non-stationary components of environmental acoustics and speech. We propose
three CNN architectures: (1) vanilla CNN that directly generates the enhanced
signal; (2) spectral-subtraction-style CNN (SS-CNN) that first predicts noise
and then generates the enhanced signal by subtracting noise from the noisy
signal; (3) Wiener-style CNN (Wiener-CNN) that generates an optimal mask for
suppressing noise. An important problem of the proposed networks is that they
introduce considerable delays, which limits their real-time application for CI
users. To address this, this study also considers causal variations of these
networks. Our experiments show that the proposed networks (both causal and
non-causal forms) achieve significant improvement over existing baseline
systems. We also found that causal Wiener-CNN outperforms other networks, and
leads to the best overall envelope coefficient measure (ECM). The proposed
algorithms represent a viable option for implementation on the CCi-MOBILE
research platform as a pre-processor for CI users in naturalistic environments.
