Infant cry classification using audio feature engineering and machine learning (SMOTE, XGBoost, ensemble voting) to predict likely baby needs such as hunger, discomfort, tiredness, burping, and belly pain.
python machine-learning signal-processing scikit-learn supervised-learning xgboost feature-engineering audio-classification librosa smote sound-classification imbalanced-dataset healthcare-ai infant-cry-classification baby-cry-analysis
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Updated
Mar 31, 2026 - Jupyter Notebook