Deep Learning Based Noise Reduction for Acoustic Hearing Preserved Cochlear Implant

• Main Authors: Wu, Tsung-Chen, 吳宗振
• Other Authors: Chi, Tai-Shih
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/92rwg6

碩士 === 國立交通大學 === 電信工程研究所 === 106 === Vocoder simulations are generally adopted to simulate the electrical hearing induced by the cochlear implant (CI). Our research group is developing a new four-electrode CI microsystem that induces high-frequency electrical hearing while preserving low-frequency acoustic hearing. To assess the functionality of this CI, a previously developed hearing-impaired (HI) hearing model is combined with a 4-channel vocoder in this thesis to respectively mimic the perceived acoustic hearing and electrical hearing. Psychoacoustic experiments are conducted on Mandarin speech recognition for determining spectral coverages of electrodes for this CI. Simulation results show that initial consonants of Mandarin are more difficult to recognize than final vowels of Mandarin via acoustic hearing of HI patients. After electrical hearing being induced through logarithmic-frequency distributed electrodes, speech intelligibility of HI patients is boosted for all Mandarin phonemes, especially for initial consonants. Similar results are consistently observed in clean and noisy test conditions. Next, we combine a deep neural network based noise reduction algorithm with the proposed CI system in the hope to improve the Mandarin speech intelligibility for seen and unseen noise types. Ultimately, we use objective evaluation and subjective evaluation scores to verify this model, hence, to provide the proof of concept of this combinational system.