TY  - JOUR
AU  - Li, Lieber Po-Hung
AU  - Han, Ji-Yan
AU  - Zheng, Wei-Zhong
AU  - Huang, Ren-Jie
AU  - Lai, Ying-Hui
PY  - 2021
DA  - 2021/10/28
TI  - Improved Environment-Aware–Based Noise Reduction System for Cochlear Implant Users Based on a Knowledge Transfer Approach: Development and Usability Study
JO  - J Med Internet Res
SP  - e25460
VL  - 23
IS  - 10
KW  - cochlear implants
KW  - noise reduction
KW  - deep learning
KW  - noise classification
KW  - hearing
KW  - deaf
KW  - sound
KW  - audio
KW  - cochlear
AB  - Background: Cochlear implant technology is a well-known approach to help deaf individuals hear speech again and can improve speech intelligibility in quiet conditions; however, it still has room for improvement in noisy conditions. More recently, it has been proven that deep learning–based noise reduction, such as noise classification and deep denoising autoencoder (NC+DDAE), can benefit the intelligibility performance of patients with cochlear implants compared to classical noise reduction algorithms. Objective: Following the successful implementation of the NC+DDAE model in our previous study, this study aimed to propose an advanced noise reduction system using knowledge transfer technology, called NC+DDAE_T; examine the proposed NC+DDAE_T noise reduction system using objective evaluations and subjective listening tests; and investigate which layer substitution of the knowledge transfer technology in the NC+DDAE_T noise reduction system provides the best outcome. Methods: The knowledge transfer technology was adopted to reduce the number of parameters of the NC+DDAE_T compared with the NC+DDAE. We investigated which layer should be substituted using short-time objective intelligibility and perceptual evaluation of speech quality scores as well as t-distributed stochastic neighbor embedding to visualize the features in each model layer. Moreover, we enrolled 10 cochlear implant users for listening tests to evaluate the benefits of the newly developed NC+DDAE_T. Results: The experimental results showed that substituting the middle layer (ie, the second layer in this study) of the noise-independent DDAE (NI-DDAE) model achieved the best performance gain regarding short-time objective intelligibility and perceptual evaluation of speech quality scores. Therefore, the parameters of layer 3 in the NI-DDAE were chosen to be replaced, thereby establishing the NC+DDAE_T. Both objective and listening test results showed that the proposed NC+DDAE_T noise reduction system achieved similar performances compared with the previous NC+DDAE in several noisy test conditions. However, the proposed NC+DDAE_T only required a quarter of the number of parameters compared to the NC+DDAE. Conclusions: This study demonstrated that knowledge transfer technology can help reduce the number of parameters in an NC+DDAE while keeping similar performance rates. This suggests that the proposed NC+DDAE_T model may reduce the implementation costs of this noise reduction system and provide more benefits for cochlear implant users. 
SN  - 1438-8871
UR  - https://www.jmir.org/2021/10/e25460
UR  - https://doi.org/10.2196/25460
UR  - http://www.ncbi.nlm.nih.gov/pubmed/34709193
DO  - 10.2196/25460
ID  - info:doi/10.2196/25460
ER  -