Abstract :

Understanding Baby Cries Is Essential, As Infants Cannot Communicate Their Needs Verbally. Traditionally, Caregivers Relied On Experience, Observation, And Intuition To Interpret Cries. However, This Approach Is Subjective, Inconsistent, And May Result In Incorrect Decisions Regarding Needs Such As Hunger, Pain, Or Discomfort. These Limitations Highlight The Necessity For An Accurate And Automated Solution. The Proposed System Introduces An Intelligent Approach For Baby Cry Classification Using Audio Signal Processing Techniques. The System Analyzes Cry Audio Signals And Extracts Meaningful Features Using Mel-Frequency Cepstral Coefficients (MFCC), Which Effectively Represent The Frequency Characteristics Of Sound. These Features Are Then Used To Train Multiple Machine Learning Algorithms, Including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree Classifier (DTC), AdaBoost (ADB), And Linear Discriminant Analysis (LDA), Enabling Comparative Performance Evaluation. To Further Enhance Classification Accuracy, A Convolutional Neural Network (CNN), A Deep Learning Model, Is Implemented As The Primary Approach. The CNN Model Is Capable Of Learning Complex Patterns And Relationships Within The Extracted Features, Leading To Improved Prediction Performance Over Traditional Machine Learning Techniques. The System’s Performance Is Assessed Using Evaluation Metrics Such As Accuracy, Precision, Recall, And F1-score. Finally, The Research Is Completed By Integrating Feature Extraction, Model Training, Evaluation, And Prediction Into A User-friendly Interface, Allowing Efficient And Reliable Identification Of Baby Cry Types To Support Caregivers In Making Timely And Informed Decisions.

Published:

10-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

2233-2243

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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