Project Title : Baby Cry Detection

Problem Statement:

Title: Baby Cry Detection for Infant Care

Problem Description:

The aim of this project is to develop a robust system for detecting the cause of a baby's cry to assist caregivers in responding effectively to the baby's needs. The primary focus will be on identifying common reasons for a baby's cry, including hunger, discomfort, belly pain, and fatigue.

Dataset:

The dataset consists of audio recordings of baby cries, categorized into different classes based on the cause of the cry. The classes include:

• Hunger
• Discomfort
• Belly Pain
• Fatigue

Each class contains a varying number of audio files corresponding to instances of the respective cause.

Objective:

The objective of this project is to build and train machine learning models to classify baby cries into their respective categories. The models will be trained on features extracted from the audio recordings to distinguish between different causes of crying.

Tasks:

1. Data Preprocessing: Load and preprocess the audio data, including feature extraction and labeling.

2. Model Development: Train multiple machine learning models, including but not limited to decision trees, random forests, support vector machines, gradient boosting machines, logistic regression, XGBoost, LGBMBoost, AdaBoost, and CatBoost.

3. Model Evaluation: Evaluate the performance of each model using metrics such as accuracy, precision, recall, and F1-score. Utilize techniques like cross-validation to ensure robustness of the models.

4. Model Selection: Select the best-performing model based on evaluation metrics and fine-tune its hyperparameters to optimize performance.

5. Deployment: Deploy the selected model into a real-time system or application that can automatically detect the cause of a baby's cry.

Deliverables:

• Trained machine learning models for baby cry classification.
• Performance evaluation reports for each model.
• Documentation detailing the development process, model selection criteria, and deployment instructions.
• A deployed system/application for real-time baby cry detection and cause identification.

Expected Outcome:

The developed system will provide caregivers with valuable insights into the reasons behind a baby's cry, enabling them to respond promptly and appropriately to the baby's needs. This can lead to improved caregiving experiences and better infant care outcomes.

Summary:

In this project, we aimed to develop a robust system for automatically detecting the cause of a baby's cry to assist caregivers in responding effectively to the baby's needs. We utilized various machine learning algorithms, including decision trees, random forests, ensemble methods (AdaBoost, XGBoost, LGBM, CatBoost), logistic regression, and support vector machines (SVM), to classify baby cries into different categories such as hunger, discomfort, belly pain, and fatigue.

Performance Evaluation:

After training and evaluating multiple models, we observed varying levels of performance across different algorithms. The decision tree model showed reasonably good performance, with balanced precision, recall, and F1-scores across different classes. Ensemble methods such as Random Forest, AdaBoost, XGBoost, LightGBM, and CatBoost consistently outperformed individual algorithms, demonstrating high precision, recall, F1-scores, and overall accuracy.

Logistic regression and SVM performed relatively poorly compared to ensemble methods, with lower precision, recall, and F1-scores across most classes and lower overall accuracy.

Conclusion:

Based on the performance evaluation, the ensemble methods, particularly Random Forest, XGBoost, LightGBM, and CatBoost, emerged as the top-performing models for classifying baby cries into different categories. These models demonstrated high precision, recall, F1-scores, and overall accuracy, indicating their effectiveness in accurately predicting the cause of a baby's cry.

The decision tree model also showed promising results and could be considered as an alternative, especially if computational efficiency is a priority.

Logistic regression and SVM, on the other hand, performed relatively poorly compared to other models and may not be suitable for this classification task.

Overall, the ensemble methods, particularly Random Forest, XGBoost, LightGBM, and CatBoost, are recommended for deployment in real-world applications for automatic baby cry detection and cause identification.

Best Performing Model:

Among the models evaluated, the Random Forest Classifier demonstrated the best performance, with high precision, recall, F1-scores, and overall accuracy across all classes. Therefore, the Random Forest Classifier is recommended as the best-performing model for the baby cry detection task in this project

Baby Cry Detection Dataset

The Baby Cry Detection dataset contains audio recordings of baby cries categorized into different classes based on the cause of the cry, such as hunger, discomfort, belly pain, and fatigue.

File Structure

The dataset includes the following files:

• baby_cry_hunger.wav: Audio recordings of baby cries due to hunger.
• baby_cry_discomfort.wav: Audio recordings of baby cries due to discomfort.
• baby_cry_belly_pain.wav: Audio recordings of baby cries due to belly pain.
• baby_cry_fatigue.wav: Audio recordings of baby cries due to fatigue.

Dataset Usage

To use the dataset, you can download the files from Kaggle and load them into your preferred programming environment. For example, you can use the librosa library in Python to load and analyze the audio recordings.

References

https://www.kaggle.com/datasets/aniruth100/baby-cry-detection

Image Downloaded References

• Pexels: Infant Cry