Classification of Baby Cry Sound Using Higuchi’s Fractal Dimension with K-Nearest Neighbor and Support Vector Machine

Abstract

The crying baby sound is a way to express their physical and psychological conditions. For most people, it is difficult to distinguish the meaning of the sound of a baby’s cry because they are almost all similar. In fact, sometimes parents still lack knowledge of understanding their baby’s condition from the sound of crying. Therefore, research to classify the types of baby crying sounds mathematically becomes very interesting. This study aimed to classify the types of crying sounds for babies using fractal dimensions, particularly the Higuchi Fractal dimension. The data used in this study were 80 data consisting of 4 types of cries, namely hunger cries, tired cries, stomach ache cries, and uncomfortable cries. In this study k-max values of 10, 16, and 50 were selected as experiments. The results of the fractal dimension value of each sound signal were carried out in the data grouping process. The k-fold cross validation data distribution method with values of k = 5 and 10 were used. The classification process was carried out using the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) methods. Based on the research results, the best accuracy was 78.75% at level 5 decomposition with 5-fold cross validation, k-max value = 10, and K = 9 value on the KNN method. Whereas with the SVM method, the best accuracy was 80% at level 5 decomposition with 10-fold cross validation, k-max value = 10 and c = 10 and using RBF kernel with γ = 10. So in this study, the Support Vector Machine (SVM) method was slightly better than the K-Nearest Neighbor (KNN) method.