Enhancing Infant Crying Detection with Gradient Boosting for Improved Emotional and Mental Health Diagnostics

Infant crying can serve as a crucial indicator of various physiological and emotional states. This paper introduces a comprehensive approach for detecting infant cries within audio data. We integrate Meta's Wav2Vec with traditional audio features, such as Mel-frequency cepstral coefficients (MFCCs), chroma, and spectral contrast, employing Gradient Boosting Machines (GBM) for cry classification. We validate our approach on a real-world dataset, demonstrating significant performance improvements over existing methods.