Audio IoT Analytics for Home Automation Safety

The aim of the paper is to perform audio analytics based on the audio sensor data that is continuously monitoring the home environment automatically through an audio Internet of Things (IoT) system. Domestic violence is one of the major problems in many cities nowadays. We have proposed a home automation system where IoT sensors records the audio in home environment continuously and the audio is sent to machine learning server where the audio is split into small clips and classified into different categories. The need of an automatic detection system is urgent for enforcing home safety and safe neighborhood. If IoT system detects any suspicious sound, it generates an emergency notification to nearest emergency services for possible action to be taken. The classification of audio such as gunshots, explosion, glass breaking, screaming and siren is based on shallow learning (Support vector machine, Decision tree, Random forest and Naïve Bayes) and deep learning (Convolutional neural network and Long short-term memory). Our experiments validated that Convolutional Neural Network shows the best performance (89% accuracy) compared to other machine learning algorithms.