Using deep learning for acoustic event classification: The case of natural disasters - Ekpezu, A.O.; Wiafe, I.; Katsriku, F.; Yaokumah, W. (JASA, 2021)
This study proposes a sound classification model for natural disasters. Deep learning techniques, a convolutional neural network (CNN) and long short-term memory (LSTM), were used to train two individual classifiers. The study was conducted using a dataset acquired online1 and truncated at 0.1 s to obtain a total of 12 937 sound segments. The result indicated that acoustic signals are effective for classifying natural disasters using machine learning techniques. The classifiers serve as an alternative effective approach to disaster classification. The CNN model obtained a classi fication accuracy of 99.96%, whereas the LSTM obtained an accuracy of 99.90%. The misclassification rates obtained in this study for the CNN and LSTM classifiers (i.e., 0.4% and 0.1%, respectively) suggest less classifica tion errors when compared to existing studies. Future studies may investigate how to implement such classifiers for the early detection of natural disasters in real time.
Research Article