Development on Deaf Support Application Based on Daily Sound Classification Using Image-based Deep Learning

Ji-Hee An - Department of Smart Information and Telecommunication Engineering, Sangmyung University, Cheonan, Chungnam, Republic of Korea
Na-Kyoung Koo - Department of Smart Information and Telecommunication Engineering, Sangmyung University, Cheonan, Chungnam, Republic of Korea
Ju-Hye Son - Department of Smart Information and Telecommunication Engineering, Sangmyung University, Cheonan, Chungnam, Republic of Korea
Hye-Min Joo - Department of Smart Information and Telecommunication Engineering, Sangmyung University, Cheonan, Chungnam, Republic of Korea
Seungdo Jeong - Department of Smart Information and Telecommunication Engineering, Sangmyung University, Cheonan, Chungnam, Republic of Korea

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According to statistics, the number of hearing-impaired persons among the disabled in Korea accounts for 27% of all persons with disabilities. However, there is insufficient support for the deaf and hard of hearing's protective devices and life aids compared to the large number. In particular, the hearing impaired misses much information obtained through sound and causes inconvenience in daily life. Therefore, in this paper, we propose a method to relieve the discomfort in the daily life of the hearing impaired. It analyzes sounds that can occur frequently and must be recognized in daily life and guide them to the hearing impaired through applications and vibration bracelets. Sound analysis was learned by using deep learning by converting sounds that often occur in daily life into the Mel-Spectrogram. The sound that actually occurs is recorded through the application, and then it is identified based on the learning result. According to the identification result, predefined alarms and vibrations are provided differently so that the hearing impaired can easily recognize it. As a result of the recognition of the four major sounds occurring in real life in the experiment, the performance showed an average of 85% and an average of 80% of the classification rate for mixed sounds. It was confirmed that the proposed method can be applied to real-life through experiments. Through the proposed method, the quality of life can be improved by allowing the hearing impaired to recognize and respond to sounds that are essential in daily life.


Sound analysis; Mel-Spectrogram; YOLO; deep learning; hearing impaired.

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