Published on in Vol 23, No 10 (2021): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25460, first published .
Improved Environment-Aware–Based Noise Reduction System for Cochlear Implant Users Based on a Knowledge Transfer Approach: Development and Usability Study

Improved Environment-Aware–Based Noise Reduction System for Cochlear Implant Users Based on a Knowledge Transfer Approach: Development and Usability Study

Improved Environment-Aware–Based Noise Reduction System for Cochlear Implant Users Based on a Knowledge Transfer Approach: Development and Usability Study

Journals

  1. Healy E, Johnson E, Pandey A, Wang D. Progress made in the efficacy and viability of deep-learning-based noise reduction. The Journal of the Acoustical Society of America 2023;153(5):2751 View
  2. Han J, Wang C, Li J, Lai Y. Ambulatory Phonation Monitoring Using Wireless Headphones With Deep Learning Technology. IEEE Systems Journal 2023;17(3):4752 View
  3. Chang Y, Han J, Chu W, Li L, Lai Y. Enhancing music recognition using deep learning-powered source separation technology for cochlear implant users. The Journal of the Acoustical Society of America 2024;155(3):1694 View
  4. Essaid B, Kheddar H, Batel N, Chowdhury M, Lakas A. Artificial Intelligence for Cochlear Implants: Review of Strategies, Challenges, and Perspectives. IEEE Access 2024;12:119015 View
  5. Frosolini A, Franz L, Caragli V, Genovese E, de Filippis C, Marioni G. Artificial Intelligence in Audiology: A Scoping Review of Current Applications and Future Directions. Sensors 2024;24(22):7126 View