CNN and Sound Processing-Based Audio Classifier for Alarm Sound Detection

Abstract

Artificial neural networks (ANN) has evolved through many stages in the last three decades with many researchers contributing in this challenging field. With the power of math, complex problems can also be solved by ANNs. ANNs like convolutional neural network (CNN), deep neural network, generative adversarial network (GAN), long short-term memory (LSTM) network, recurrent neural network (RNN), ordinary differential network, etc., are playing promising roles in many MNCs and IT industries for their predictions and accuracy. In this paper, convolutional neural network is used for prediction of abnormal hospital instrumental beep sounds in high noise levels. Based on supervised learning, the research has developed the novel CNN architecture for beep sound recognition in noisy situations. The proposed method gives better results with an accuracy of 96%. The prototype is tested with various architecture models for the training and test data, out of which two-layer CNN classifier predictions were the best.