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

恢复声音的感觉：基于深度学习的人工耳蜗声音电神经传输补偿策略

基本信息

批准号：
MR/T03095X/1
负责人：
Tobias Goehring
金额：
$ 132.18万
依托单位：
University of Cambridge
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2020
资助国家：
英国
起止时间：
2020 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=MR%2FT03095X%2F1
关键词：
Restoring sense sound deep learning

项目摘要

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.

人工耳蜗通过电刺激听觉神经来代替受损的感觉毛细胞，为严重或深度耳聋的人提供声音感觉。人工耳蜗是改变生活的设备，全球有超过50万人正在使用人工耳蜗。人工耳蜗可以很好地帮助使用者在安静的环境中理解语音，但在嘈杂的环境中，比如繁忙的街道或餐馆，使用人工耳蜗的人可能很难理解语音。这是一个对人工耳蜗使用者的生活质量有负面影响的重要问题。我将通过开发一种在背景噪声中识别和增强语音的策略来帮助解决这个问题。该策略将建立在多学科方法的基础上，将模拟听觉神经对电刺激反应的计算模型与训练有素的机器学习算法相结合，以减少干扰噪声。我将使用由真实语音和噪声记录组成的音频数据来生成大量的训练数据，用于教算法如何在噪声中检测和保存语音。重要的是，该策略将通过模拟人工耳蜗处理及其对听神经的刺激的计算模型，针对人工耳蜗用户的特定要求进行优化。我将设计这个策略，这样它就可以集成到人工耳蜗的外部语音处理器中，而不需要再进行手术植入。该项目将有助于更好地了解人工耳蜗的声音传输，并有助于克服人工耳蜗使用者在日常生活中面临的交流挑战。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Improved tactile speech robustness to background noise with a dual-path recurrent neural network noise-reduction strategy

通过双路径循环神经网络降噪策略提高触觉语音对背景噪声的鲁棒性

DOI：
10.21203/rs.3.rs-3395475/v1
发表时间：
2023
期刊：
影响因子：
0
作者：
Fletcher M
通讯作者：
Fletcher M

Cochlear Implant Research and Development in the Twenty-first Century: A Critical Update.

DOI：
10.1007/s10162-021-00811-5
发表时间：
2021-10
期刊：
Journal of the Association for Research in Otolaryngology : JARO
影响因子：
0
作者：
Carlyon RP;Goehring T
通讯作者：
Goehring T

The Panoramic ECAP Method: Estimating Patient-Specific Patterns of Current Spread and Neural Health in Cochlear Implant Users.

DOI：
10.1007/s10162-021-00795-2
发表时间：
2021-10
期刊：
Journal of the Association for Research in Otolaryngology : JARO
影响因子：
0
作者：
Garcia C;Goehring T;Cosentino S;Turner RE;Deeks JM;Brochier T;Rughooputh T;Bance M;Carlyon RP
通讯作者：
Carlyon RP

The Effect of Free-Field Presentation and Processing Strategy on a Measure of Spectro-Temporal Processing by Cochlear-Implant Listeners.