A New Approach for Noise Suppression in Cochlear Implants: A Single-Channel Noise Reduction Algorithm1 - Zhou, Huali; Wang, Ningyuan; Zheng, Nengheng; Yu, Guangzheng; Meng, Qinglin (Frontiers in Neuroscience, 2020)
The cochlea “translates” the in-air vibrational acoustic “language” into the spikes of neural “language” that are then transmitted to the brain for auditory understanding and/or perception. During this intracochlear “translation” process, high resolution in time-frequency-intensity domains guarantees the high quality of the input neural information for the brain, which is vital for our outstanding hearing abilities. However, cochlear implants (CIs) have coarse artificial coding and interfaces, and CI users experience more challenges in common acoustic environments than their normal-hearing (NH) peers. Noise from sound sources that a listener has no interest in may be neglected by NH listeners, but they may distract a CI user. We discuss the CI noise suppression techniques and introduce noise management for a new implant system. The monaural signal-to-noise ratio estimation-based noise suppression algorithm “eVoice,” which is incorporated in the processors of Nurotron® Enduro™, was evaluated in two speech perception experiments. The results show that speech intelligibility in stationary speech-shaped noise can be significantly improved with eVoice. Similar results have been observed in other CI devices with single-channel noise reduction techniques. Specifically, the mean speech reception threshold decrease in the present study was 2.2 dB. The Nurotron society already has more than 10,000 users, and eVoice is a start for noise management in the new system. Future steps on nonstationary-noise suppression, spatial-source separation, bilateral hearing, microphone configuration, and environment-specification are warranted. The existing evidence, including our research, suggests that noise suppression techniques should be applied in CI systems. The artificial hearing of CI listeners requires more advanced signal processing techniques to reduce brain effort and increase intelligibility in noisy settings.