Deep Learning-Based Hazardous Sound Classification for the Hard of Hearing and Deaf
2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)(2018)
摘要
The hard of hearing or deaf can only access limited auditory information in dangerous situations. Therefore, development of a system for sensing hazardous auditory information may be of great help to them. However, such systems have focused on effective signal transduction when a hazardous sound is detected, and the classification of hazardous sounds has been less investigated. The present study was conducted to classify sounds by using Recurrent Neural Network (RNN)-based models, Convolutional Neural Network (CNN)-based models, the combination of the two models, and ensemble models prepared by combining various models. The experimental results showed that the accuracy of the 3-layer Long Short-Term Memory (LSTM) model was 97.63% and that of the ensemble model was 98.00%. As an attempt at real-life application of the developed model, a warning system was prepared by using Raspberry Pi and a vibrator.
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关键词
Predictive models,Feature extraction,Data models,Hardware,Auditory system,Artificial neural networks,Automobiles
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