Restoring the sense of sound: deep-learning based compensation strategies for the electro-neural transmission of sound by cochlear implants

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Cochlear implants provide a sense of sound to people who are severely or profoundly deaf, by electrically stimulating the auditory nerve in place of the damaged sensory hair cells. Cochlear implants are life changing devices and more than half a million people are using a cochlear implant globally. Cochlear implants work well for helping users to understand speech in quiet situations but in noisy environments, such as a busy street or a restaurant, someone using a cochlear implant is likely to struggle to comprehend speech. This is an important problem with negative implications for the quality of life of cochlear implant users. I will help to address this problem by developing a strategy that can identify and enhance speech in background noise. The strategy will build on a multi-disciplinary approach that combines computational models simulating the auditory nerve response to electrical stimulation, with machine-learning algorithms trained to reduce interfering noise. I will use audio data consisting of realistic speech and noise recordings to generate large amounts of training data for teaching the algorithm how to detect and preserve speech in noise. Importantly, the strategy will be optimised for the specific requirements of cochlear-implant users with a computational model that simulates the cochlear implant processing and its stimulation of the auditory nerve. I will design the strategy so that it can be integrated into the external speech processor of a cochlear implant without the need for surgical re-implantation. This project will contribute to a better understanding of the transmission of sound by cochlear implants and help to overcome the communication challenges that cochlear implant users face in their daily lives.