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.
@article{arxiv.2410.09236,
title = {Enhancing Infant Crying Detection with Gradient Boosting for Improved Emotional and Mental Health Diagnostics},
author = {Kyunghun Lee and Lauren M. Henry and Eleanor Hansen and Elizabeth Tandilashvili and Lauren S. Wakschlag and Elizabeth Norton and Daniel S. Pine and Melissa A. Brotman and Francisco Pereira},
journal= {arXiv preprint arXiv:2410.09236},
year = {2026}
}