@inproceedings{705ba59a53b04cda9d062794dfa1ad5f,
title = "Automatic Sound Alarm Classification Using Deep Learning For the Deaf and Hard of Hearing",
abstract = "Hearing is a very important sensory function for humans. Deaf people who fail to recognize signals indicating dangerous situations around them can endanger their lives, such as building fire alarm signals, gas leak alarms, tsunami alarms, and other dangerous alarms. This research proposes a tool system that can recognize and classify alarm sounds automatically. The two deep learning models we propose for the alarm sound classification task are CNN (Convolutional Neural Network) and LSTM (Long Short Term Memory). The models are trained by using the Mel-Spectrogram extracted from the alarm audio dataset. Based on this experiment, CNN gets better accuracy, reaching 98.83% while the accuracy of the LSTM model is 96.66%. Then the best model will be deployed to the Raspberry Pi, which uses a low-cost microphone for real-time applications.",
keywords = "CNN, LSTM, Raspberry Pi, alarm, automatic, classification, deaf, deep learning, real-time",
author = "Nugroho, {Devis Styo} and Hendra Kusuma and Sardjono, {Tri Arief}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 International Conference of Science and Information Technology in Smart Administration, ICSINTESA 2022 ; Conference date: 10-11-2022 Through 12-11-2022",
year = "2022",
doi = "10.1109/ICSINTESA56431.2022.10041679",
language = "English",
series = "2022 International Conference of Science and Information Technology in Smart Administration, ICSINTESA 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "133--138",
booktitle = "2022 International Conference of Science and Information Technology in Smart Administration, ICSINTESA 2022",
address = "United States",
}