A Scene Adaption Framework for Infant Cry Detection in Obstetrics

Dongmin Huang1; Lirong Ren2; Hongzhou Lu3; Wenjin Wang1

10.1109/EMBC40787.2023.10340693

2023

45th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

2375-7477

1558-4615

979-8-3503-2448-8

2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

1-5

24-27 July 2023

Sydney, Australia

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Infant cry provides useful clinical insights for caregivers to make appropriate medical decisions, such as in obstetrics. However, robust infant cry detection in real clinical settings (e.g. obstetrics) is still challenging due to the limited training data in this scenario. In this paper, we propose a scene adaption framework (SAF) including two different learning stages that can quickly adapt the cry detection model to a new environment. The first stage uses the acoustic principle that mixture sources in audio signals are approximately additive to imitate the sounds in clinical settings using public datasets. The second stage utilizes mutual learning to mine the shared characteristics of infant cry between the clinical setting and public dataset to adapt the scene in an unsupervised manner. The clinical trial was conducted in Obstetrics, where the crying audios from 200 infants were collected. The experimented four classifiers used for infant cry detection have nearly 30% improvement on the F1-score by using SAF, which achieves similar performance as the supervised learning based on the target setting. SAF is demonstrated to be an effective plugand-play tool for improving infant cry detection in new clinical settings. Our code is available at https://github.com/contactlesshealthcare/Scene-Adaption-for-Infant-Cry-Detection.

Pediatrics Adaptation models Codes Biological system modeling Supervised learning Training data Clinical trials

第一

英语

EI ; CPCI-S

National Key R&D Program of China[2022YFC2407800]

Computer Science ; Engineering

Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical

WOS:001133788303042

20240215361636

Audio acoustics ; Learning systems

Medicine and Pharmacology:461.6 ; Acoustic Waves:751.1

IEEE

1.Department of Biomedical Engineering, Southern University of Science and Technology, China
2.Department of Obstetrics, Baoan Hospital of Traditional Chinese Medicine in Shenzhen, China
3.The Third People’s Hospital of Shenzhen, China

Dongmin Huang,Lirong Ren,Hongzhou Lu,et al. A Scene Adaption Framework for Infant Cry Detection in Obstetrics[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2023:1-5.