ArticleProceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)2022
INFANT CRYING DETECTION IN REAL-WORLD ENVIRONMENTS.
Article in Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The application of machine learning for infant cries classification and pathological cries detection: A systematic review.Science progressPooled it
- 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
- Deep learning in fetal, infant, and toddler neuroimaging research.Developmental cognitive neuroscience · 2026Review
- Everyday maternal sensitivity to distress is dynamic and systematically varies across contexts.Child development · 2026Article
- Automated Analysis of Naturalistic Recordings in Early Childhood: Applications, Challenges, and Opportunities.IEEE signal processing magazine · 2025Article
- Machine learning-based infant crying interpretation.Frontiers in artificial intelligence · 2024Article
- Concurrent and prospective associations between infant frontoparietal and default mode network connectivity and negative affectivity.Biological psychology · 2023Article
- Validating a model to detect infant crying from naturalistic audio.Behavior research methods · 2023Article
- Towards Robust Family-Infant Audio Analysis Based on Unsupervised Pretraining of Wav2vec 2.0 on Large-Scale Unlabeled Family Audio.Interspeech · 2023Article
- Infant Cry Signal Diagnostic System Using Deep Learning and Fused Features.Diagnostics (Basel, Switzerland) · 2023Article
- Auditory chaos classification in real-world environments.Frontiers in digital health · 2023Article
- An ensemble learning approach to digital corona virus preliminary screening from cough sounds.Scientific reports · 2021Article
- Caregiver Holding, Not Vocalizing, Supports Real-Time Vagal Regulation.Infancy : the official journal of the International Society on Infant StudiesArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Most existing cry detection models have been tested with data collected in controlled settings. Thus, the extent to which they generalize to noisy and lived environments is unclear. In this paper, we evaluate several established machine learning approaches including a model leveraging both deep spectrum and acoustic features. This model was able to recognize crying events with F1 score 0.613 (Precision: 0.672, Recall: 0.552), showing improved external validity over existing methods at cry detection in everyday real-world settings. As part of our evaluation, we collect and annotate a novel dataset of infant crying compiled from over 780 hours of labeled real-world audio data, captured via recorders worn by infants in their homes, which we make publicly available. Our findings confirm that a cry detection model trained on in-lab data underperforms when presented with real-world data (in-lab test F1: 0.656, real-world test F1: 0.236), highlighting the value of our new dataset and model.
Indexed as
Identifiers
What OpenQuestion holds
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.