Infant Cry Language Analysis and Recognition: An Experimental Approach (www.ieee-jas.net)

Researchers have developed an experimental approach to analyze and recognize infant cry language using advanced acoustic signal processing techniques, aiming to decode the underlying causes and emotional states conveyed through infant cries. This study leverages machine learning algorithms applied to features extracted from cry signals, such as Mel-frequency cepstral coefficients (MFCCs) and scalogram representations, to classify different types of cries associated with distress, hunger, or pain. The approach integrates established acoustic measures with deep learning models, improving accuracy in distinguishing subtle variations in crying patterns. This work is significant for the AI/ML community as it addresses a challenging real-world problem combining speech signal processing, emotion recognition, and healthcare applications. By automating infant cry analysis, the system has potential to aid caregivers and medical professionals in early diagnosis of neonatal health issues or discomfort, thus providing timely interventions. Technically, the study demonstrates how traditional digital signal processing tools, when fused with modern deep learning frameworks, can effectively handle noisy, non-verbal acoustic data typical in infant cry recordings. The research highlights the value of interdisciplinary methods that merge developmental psychology insights with computational models, pushing forward the frontier of audio-based affective computing. Such advancements could pave the way for smart incubators and monitoring devices equipped with real-time cry recognition systems, ultimately improving infant care and advancing human-centered AI applications in healthcare.