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arXiv · 2410.09236

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

Kyunghun Lee·Lauren M. Henry·Eleanor Hansen·Elizabeth Tandilashvili·Lauren S. Wakschlag·Elizabeth Norton·Daniel S. Pine·Melissa A. Brotman·Francisco Pereira
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Abstract

Infant crying can serve as a crucial indicator of various physiological and emotional states. This paper introduces a comprehensive approach detecting infant cries within audio data. We integrate Wav2Vec with traditional audio features and employ Gradient Boosting Machines for cry classification. We validate our approach on a real world dataset, demonstrating significant performance improvements over existing methods.

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Kyunghun Lee, Lauren M. Henry, Eleanor Hansen, Elizabeth Tandilashvili, Lauren S. Wakschlag, Elizabeth Norton, Daniel S. Pine, Melissa A. Brotman, Francisco Pereira. 2024-10-11. Enhancing Infant Crying Detection with Gradient Boosting for Improved Emotional and Mental Health Diagnostics. https://arxiv.org/abs/2410.09236

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