Smart Nursery for Smart Cities: Infant Sound Classification Based on Novel Features and Support Vector Classifier

In the age of smart cities, it is envisioned that most processes within the smart city context will be smart and automated. This includes smart houses, smart kitchens, etc. within this context, a need will arise for Smart nursery rooms. Within the smart nursery concept, the infant needs will need to be fulfilled automatically, in addition, to infant monitoring and safety. The motivation of this work is to design a smart cradle system for a smart nursery room that automates the functions of the cradle based on the infant's sounds. Therefore, in this paper, we propose an infant sound classification technique based on the Support Vector Classifier (SVC) with Radial Basis Function (RBF) kernel using 18 extracted features of infant sounds. The proposed technique has been compared with two SVC kernel function, linear, and poly, as well as other classification algorithms including Decision Tree, Random Forest, and Gaussian Naive Bayes. As a result of comparing the confusion matrix, recall, F1 Score, accuracies, and precision values of various applied machine learning algorithms over-extracted features. SVC using RBF kernel function was found to be the most efficient model with an average accuracy of more than 96%. The proposed system outperforms all other systems proposed in the previous literature.