Infant Cry Classification Using Semi-supervised K-Nearest Neighbor Approach

Infants cry for many different reasons. Understanding the infant's language is a critical challenge that many parents suffer from, thus, it is hard to know precisely for what reason infants are crying. The purpose of our study is to determine whether the infant cry is due to hunger or not, using semi-supervised machine learning techniques. There are two commonly used datasets in the literature, the Dunstan Baby Language and Baby Chillanto database. The total length of each of the datasets is only between 8 and 32 minutes, which is very short. For this reason, we proposed a semi-supervised learning approach (also known as self-training), which can increase the dataset by classifying the unlabeled data from Google AudioSet. We have chosen the k-nearest neighbors (KNN) classifier to determine whether the cry is due to hunger or not. The KNN is known to produce low-performance results if trained with limited data. Thus, we proposed our semi-supervised k-nearest neighbor (SSKNN) that can benefit from unlabeled data to increase the training set. As for feature extraction, we chose Mel Frequency Cepstral Coefficient. To evaluate the performance of the semi-supervised approach, we used the supervised KNN as our baseline model and compared the accuracy between the two approaches. The SSKNN yields better accuracy, which is 94% compared to the supervised KNN which has only an accuracy of 87%.