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

Hashemi, SMH, Kolivand, H ORCID: 0000-0001-5460-5679, Khan, W ORCID: 0000-0002-7511-3873 and Saba, T (2025) Infant Cry Analysis: A Survey of Datasets, Features, and Machine Learning Techniques. IEEE Transactions on Affective Computing, 14 (8). pp. 1-20. ISSN 1949-3045

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Abstract

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.

Item Type:	Article
Additional Information:	© 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Uncontrolled Keywords:	4605 Data Management and Data Science; 46 Information and Computing Sciences; 4611 Machine Learning; Machine Learning and Artificial Intelligence; Pediatric; Perinatal Period - Conditions Originating in Perinatal Period; Networking and Information Technology R&D (NITRD); Preterm, Low Birth Weight and Health of the Newborn; Behavioral and Social Science; Generic health relevance; Reproductive health and childbirth; 3 Good Health and Well Being; 0801 Artificial Intelligence and Image Processing; 0806 Information Systems; 1702 Cognitive Sciences; 4602 Artificial intelligence; 4603 Computer vision and multimedia computation; 4608 Human-centred computing
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RJ Pediatrics > RJ101 Child Health. Child health services R Medicine > RT Nursing
Divisions:	Computer Science and Mathematics
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Date of acceptance:	13 November 2025
Date of first compliant Open Access:	3 December 2025
Date Deposited:	03 Dec 2025 10:38
Last Modified:	04 Dec 2025 10:02
DOI or ID number:	10.1109/taffc.2025.3636598
URI:	https://researchonline.ljmu.ac.uk/id/eprint/27659

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