A Scene Adaption Framework for Infant Cry Detection in Obstetrics.

Area of Science:

• Medical acoustics
• Machine learning in healthcare
• Infant health monitoring

Background:

• Infant cry analysis offers critical clinical insights for medical decisions, particularly in obstetrics.
• Current infant cry detection models struggle in real clinical settings due to limited, specific training data.
• Developing robust cry detection is essential for timely and accurate caregiver interventions.

Purpose of the Study:

• To propose a Scene Adaptation Framework (SAF) for rapidly adapting infant cry detection models to new clinical environments.
• To address the challenge of limited training data in real-world clinical scenarios for infant cry detection.
• To enhance the F1-score performance of infant cry detection classifiers in obstetrics.

Main Methods:

• SAF employs a two-stage learning process: imitating clinical sounds using public datasets via acoustic principles and unsupervised adaptation using mutual learning.
• The first stage leverages the additive nature of audio signal mixtures to simulate clinical acoustics.
• The second stage uses mutual learning to extract shared infant cry features between clinical and public datasets.

Main Results:

• A clinical trial in Obstetrics with 200 infants demonstrated significant improvements in cry detection.
• Four tested classifiers showed nearly a 30% increase in F1-score when utilizing the SAF.
• SAF achieved performance comparable to supervised learning models trained directly on target clinical data.

Conclusions:

• The Scene Adaptation Framework (SAF) is an effective, plug-and-play solution for enhancing infant cry detection in novel clinical settings.
• SAF significantly improves classifier performance, overcoming limitations posed by scarce clinical training data.
• The framework demonstrates the potential for broader application in adapting AI models to specific healthcare environments.