AI Headphone Design for the Hearing-Impaired
Korean Industrial Technology Convergence Society, Hyun-Don Kim · Korea Industrial Technology Convergence Society · 2022
We designed an artificial intelligence (AI) headphone for hearing impaired people and proposed a convolution neural network (CNN)-based sound classifier with a low computational network that can run on an embedded PC (Raspberry Pi 4B) in real-time. Because our AI headphone can classify 20 types of dangerous or environmental sounds (e.g., siren, car horn, scream, gunshot, etc.) and recognize specific voice keywords (e.g., person name, be careful!, stop!, etc.), it can assist hearing impaired people in reducing the risk of accident exposure and improving convenience. In addition, we use vibration codes to notify the hearing impaired person of detected information to the vibration motors attached to both sides of the headphone. We confirm that our proposed sound classifier achieves an average accuracy rate of approximately 95.14%, and enables real-time processing on the Raspberry Pi 4B because it requires an average computation time of approximately 0.139s with audio recording data for 5.12s.