Abstract :

Interpreting Infant Cries Is A Critical Yet Challenging Task, As Babies Lack The Ability To Communicate Their Needs Verbally. Traditionally, Caregivers Depend On Personal Experience, Observation, And Intuition To Understand These Cries; However, Such Methods Are Inherently Subjective, Often Inconsistent, And Can Lead To Misinterpretation Of Essential Needs Like Hunger, Pain, Or Discomfort. To Overcome These Limitations, The Proposed System Presents An Intelligent And Automated Approach For Baby Cry Classification Using Advanced Audio Signal Processing Techniques. The System Processes Recorded Cry Signals And Extracts Discriminative Acoustic Features Through Mel-Frequency Cepstral Coefficients (MFCC), Which Effectively Capture The Underlying Frequency Patterns Of Audio. These Extracted Features Are Then Utilized To Train And Evaluate Multiple Machine Learning Models, Including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree Classifier (DTC), AdaBoost (ADB), And Linear Discriminant Analysis (LDA), Enabling A Comprehensive Comparative Analysis Of Their Performance. To Further Improve Classification Accuracy And Robustness, A Convolutional Neural Network (CNN) Is Employed As The Primary Model, Leveraging Its Capability To Automatically Learn Complex Feature Representations And Temporal Patterns Within Audio Data. The System’s Effectiveness Is Measured Using Key Performance Metrics Such As Accuracy, Precision, Recall, And F1-score, Ensuring Reliable Evaluation. Additionally, The Entire Framework Is Integrated Into A User-friendly Interface That Seamlessly Combines Feature Extraction, Model Training, Evaluation, And Real-time Prediction. This Endto-end Solution Provides A Consistent, Efficient, And Accurate Method For Identifying Infant Cry Types, Thereby Assisting Caregivers In Making Timely And Informed Decisions For Better Infant Care.

Published:

24-4-1-2026

Issue:

Vol. 26 No. 4-1 (2026)

Page Nos:

805-814

Section:

Articles

License:

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

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