The interpretation of baby cries is a vital yet challenging task in caregiving. Babies express their needs—hunger, sleepiness, discomfort, or happiness—through crying, but decoding these emotional cues is subjective and prone to error. This paper introduces a machine learning-based systemtoanalyze baby cries,classify their emotional states, andrecommend emotion-specific lullabies to caregivers. Utilizing audio feature extraction techniques like spectral centroid, Mel-Frequency Cepstral Coefficients (MFCCs), and RMS intensity, coupled with a Random Forest Classifier, the system achieves remarkable accuracy and robustness. Deployed viaa Django-based web application, the systemensures accessibility and real-time responsiveness, making it suitable for diverse caregiving contexts. Extensive evaluation in both controlled and noisy environments confirms the system\'s effectiveness. By bridging traditional caregiving practices with cutting-edge technology, this research offers a noveltool for enhancing infant care while preserving the cultural richness of lullabies.
Introduction
This research presents a machine learning-based system designed to accurately interpret infant cries, identifying emotional states such as hunger, discomfort, sleepiness, and happiness. Traditional methods of interpreting baby cries often rely on subjective judgment and can be inconsistent. This system utilizes advanced audio processing techniques and machine learning algorithms to provide objective, real-time analysis of infant cries.
Key Components:
Audio Feature Extraction: The system employs the Librosa library to extract key audio features from baby cries, including Mel-Frequency Cepstral Coefficients (MFCCs), spectral centroid, and Root Mean Square (RMS) intensity. These features capture the timbral characteristics, brightness, and energy of the cry, respectively.
Classification Model: A Random Forest Classifier processes the extracted features to classify the cries into emotional categories. This ensemble learning method is chosen for its robustness and effectiveness in handling complex datasets.
Real-Time Processing: The system is implemented as a web-based application using Django, enabling caregivers to upload audio recordings of baby cries for immediate analysis and classification.
Lullaby Recommendations: Based on the detected emotional state, the system recommends tailored lullabies from a curated database, providing an immediate soothing response mechanism for caregivers.
Performance Evaluation:
The system achieved an average accuracy of 91% in classifying baby cries into five emotional states. Even in noisy environments, it maintained an accuracy of 88%, demonstrating its robustness and reliability. User feedback indicated that the lullaby recommendations effectively soothed infants and reduced caregiver response times.
Conclusion
This research introduces a novel approach to infant care by integrating machine learning with cultural practices. The system not only achieves high accuracy in detecting emotional states from baby cries but also provides immediate and actionable recommendations through tailored lullabies. This integration enhances caregiving efficiency, reduces caregiver stress, and promotes infant well-being.
The cultural significanceof lullabies adds an emotional and developmental dimension to the system, bridging traditional practices with modern technology. Future research will focus on expanding the dataset to include more diverse emotional states and exploring deep learning techniques for enhanced granularity in predictions. The potential integration of IoT devices and multilingual support could further broaden the system’s applicability, making it a global solution for infant care.
This study demonstrates how artificial intelligence can revolutionize caregiving, offering a scalable, accessible, and emotionally resonant tool for modern families.
References
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