A Review of Infant Cry Recognition and Classification based on Computer-Aided Diagnoses

Crying is the only way babies communicate about their communication or health status. Infant cry may be caused by medical or physical factors. Infant cry recognition and classification (ICRC) is the determination of various physical and physiological states of infants through computer-aided detection with the help of cry sound signals. This study is a review of the studies on the ICRC. To increase the scope of the study, the year limitation was not used in the literature review. While examining the studies in the literature, the datasets used, the feature sets, the classifier used, and the success rates obtained were examined in detail. After the review, the methods used for the ICRC were summarized to guide future studies, and an overview of the developments was presented to the researchers. In addition, the findings of the studies examined are given comparatively. As a result of this study, it has been determined that the most used traditional methods in ICRC are MFCC as a feature set and neural-network-based classifiers as classifiers. In recent years, it has been determined that mostly deep learning-based methods have started to be used.