ArticleBehavior research methods2023
Validating a model to detect infant crying from naturalistic audio.
Article in Behavior research methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- From data to discovery: Technology propels speech-language research and theory-building in developmental science.Neuroscience and biobehavioral reviews · 2025Pooled it
- Bridging Machine Learning and Event-Based Analyses to Assess Maternal Contingent Responses to Infant Nondistress and Distress Vocalizations From Home Audio Recordings.Developmental science · 2026Article
- Variation in Everyday Maternal Sensitivity Predicts Infants' Real-Time Physiological Regulation.Developmental psychobiology · 2026Article
- Thinking Critically About Algorithms for Automated Detection of Behavior: 11 Guidelines for Social and Behavioral Scientists.Developmental science · 2026Article
- Everyday maternal sensitivity to distress is dynamic and systematically varies across contexts.Child development · 2026Article
- The significance of an infant's cry: a narrative review of physiological, pathological, and analytical perspectives.Frontiers in pediatrics · 2025Review
- Classification of Infant Crying Sounds Using SE-ResNet-Transformer.Sensors (Basel, Switzerland) · 2024Article
- Maternal contingent responses to distress facilitate infant soothing but not in mothers with depression or infants high in negative affect.Developmental psychology · 2024Article
- The Home Environments of Infants of Mothers with Early, Remitted Clinical Depression and No Depression during the First Two Years Postpartum.Children (Basel, Switzerland) · 2023Article
- Infant Cry Signal Diagnostic System Using Deep Learning and Fused Features.Diagnostics (Basel, Switzerland) · 2023Article
- Infant crying predicts real-time fluctuations in maternal mental health in ecologically valid home settings.Developmental psychology · 2023Article
- Auditory chaos classification in real-world environments.Frontiers in digital health · 2023Article
- INFANT CRYING DETECTION IN REAL-WORLD ENVIRONMENTS.Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference) · 2022Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Human infant crying evolved as a signal to elicit parental care and actively influences caregiving behaviors as well as infant-caregiver interactions. Automated cry detection algorithms have become more popular in recent decades, and while some models exist, they have not been evaluated thoroughly on daylong naturalistic audio recordings. Here, we validate a novel deep learning cry detection model by testing it in assessment scenarios important to developmental researchers. We also evaluate the deep learning model's performance relative to LENA's cry classifier, one of the most commonly used commercial software systems for quantifying child crying. Broadly, we found that both deep learning and LENA model outputs showed convergent validity with human annotations of infant crying. However, the deep learning model had substantially higher accuracy metrics (recall, F1, kappa) and stronger correlations with human annotations at all timescales tested (24 h, 1 h, and 5 min) relative to LENA. On average, LENA underestimated infant crying by 50 min every 24 h relative to human annotations and the deep learning model. Additionally, daily infant crying times detected by both automated models were lower than parent-report estimates in the literature. We provide recommendations and solutions for leveraging automated algorithms to detect infant crying in the home and make our training data and model code open source and publicly available.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.