Work

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

Year: 2024

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2410.09236

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

Institutions: National Institute of Mental Health, National Institute of Mental Health, National Institute of Mental Health, Northwestern University, Northwestern University

Language: English

Topic: Infant Health and Development

Subfield: Pharmacy

Field: Health Professions

Domain: Health Sciences

Open Access status: green

OpenAlex ID: W4403564475

Cites:

Cited by: 1

Related to: 10