Machine Learning Methods for Recognizing Infant Cries: An Extensive Examination of Audio Signal Processing
Bibliographic record
Abstract
This article presents a comprehensive review of recent advancements in interpreting and classifying Infant Cry Audio Signals (ICAS), aiming to support early diagnosis of health conditions in newborns. The core issue addressed is the difficulty in identifying the cause of an infant’s cry, whether it signals hunger, pain, discomfort, or illness, due to high variability in cry patterns, background noise, and limited annotated datasets. To overcome these challenges, the study explores how modern Deep Learning (DL) and Machine Learning (ML) models, such as Convolutional Neural Networks (CNN), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Gaussian Mixture Models (GMM), are applied to analyze cry features like Mel Frequency Cepstral Coefficients (MFCC) and spectrograms. It also discusses crucial stages like data collection, preprocessing, and feature extraction. The outcome of this work is a clear identification of current limitations and research gaps, especially in real-time, noise-resilient, and accurately labeled cry data processing. The study concludes with a proposal to develop a reliable, intelligent infant monitoring system using neural network architectures, aimed at assisting caretakers and healthcare professionals in timely decision-making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".