Infant Cry Signal Detection And Classification Using Deep Learning
Bibliographic record
Abstract
Detection of infant cries in noisy environments such as homes, hospitals and clinics is vital to determine the reason of baby's cry.Also, It is crucial to classify the detected cry signals into normal or pathological cries especially in the first months of the baby life.This paper proposes a deep learning automatic infant cry detection and classification system under noisy conditions.It classifies the detected cry signals into normal , asphyxia and deaf cry signals .The overall system is composed of two stages ; cry detection stage and cry classification stage.In first stage, features(Mel-frequency cepstrum coefficients MFCC) are extracted from audio signals collected from a daily life dataset and passed into a 2D-two layers convolutional neural network(2DCNN) to be classified into cry and non cry signals.In second stage , a 2D-three layers CNN is used to classify cry signals collected from dataset with cry segments only into Normal (N) , Asphyxia (A) and Deaf (D) signals according to extracted MFCC features .In first stage, Testing results show that the cry detection system reaches an accuracy of 99.59 % for classifying the signals into cry and non-cry .In second stage, Due to the lack of pathological cry signals datasets that are collected in noisy environments, we added different levels of white noise to the training dataset.This way, we were able to get more realistic results.In particular, our cry classification system achieves accuracy of 91.3% ,94.2% , 95.07 % (under white noise) with signal-to-noise ratio(SNR) of 5db, 10db and 15db, respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".