A Noise-Robust Signal Processing Strategy for Cochlear Implants Using Neural Networks

Signal processing strategies in most clinical cochlear implants (CIs) extract and transmit speech envelopes to stimulate the auditory neurons. The incomplete representation of the rich fine structures in speech has significantly degraded the CI recipients’ ability in high- level perception, including their speech understanding in noise. This paper presents a noise-robust signal processing strategy to deal with this problem. Neural networks (NN) are built and trained to simulate the advanced combination encoder (ACE, a strategy for CI products of Cochlear Corporation). The NN-based ACE (namely, NNACE) is trained with a sophisticatedly designed loss function to output envelope-like signals that 1) is compatible with ACE-based CI system and can serve as the modulator to generate the electric stimuli, 2) is more noise-robust, and 3) might bear a certain degree of the temporal fine structures of speech. Subjective and objective evaluations with vocoder simulated speech show that NNACE outperforms the other methods and further actual CI experiments are warranted.