Infant Cry Analysis: A Survey of Datasets, Features, and Machine Learning Techniques

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Knowledge about infant language can go a long way in supporting parents, nurses, and care providers in improving babies' health conditions. Crying is the most effective tool through which babies convey their requirements. In this work, several studies dealing with infant cry detection and classification are contrasted. Research demonstrates that machine learning techniques can effectively categorize and classify infant needs and certain disorders. Several datasets, including Baby Chillanto, Donate A Cry Corpus and Dunstan Baby Language, are presented. After reviewing existing Datasets, preprocessing methodologies and audio feature extraction such as MFCC, RMS energy, etc., are discussed. For infant cry detection and classification, several algorithms, such as support vector machines (SVM), convolutional neural networks (CNN), k-nearest neighbors (KNN), Random Forest, etc., have been analyzed and utilized for such processes in general. Finally, the study explores various applications of infant cry analysis, highlighting its potential to improve infant care and facilitate early diagnosis. As a result of the findings, it has been observed that infant cry analysis can effectively identify different needs and potential health concerns with high accuracy. These machine learning models' classification outputs have the potential to (1) improve childcare practices, (2) detect medical issues earlier, and (3) monitor infants continuously. These features give medical professionals and caregivers useful information for prompt intervention. The implementation of these findings can be applied in hospitals, neonatal intensive care units (NICUs), smart baby monitoring systems, and research studies focused on early childhood development.

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