Task-Space Domain Adaptation for Cross-Scene Infant Cry Detection.

Infant cry detection is important for infant daily care as the cry signals provide useful information, such as hunger and discomfort, and for medical diagnosis, such as asphyxia. However, existing detection models suffer from performance degradation caused by biased data distribution across different scenes of deployment. This paper presents a Task-Space Domain Adaptation (TSDA) method that facilitates efficient knowledge transfer from the labeled source domain (e.g., home scene) to enhance the performance in the unlabeled target domain (e.g., hospital scene). TSDA first decomposes source domain features guided by cry detection labels to obtain task-relevant features. It then employs adversarial learning with nuclear norm-based representation to align target domain features with task-relevant source features, enabling unsupervised target domain adaptation. Extensive experiments conducted on home and clinical datasets show that TSDA outperforms the direct cross-dataset baseline test by approximately 30% in F1-score, and achieves a 1-12% improvement over existing domain adaptation methods, approaching the performance of supervised learning based on the labeled target domain. This study provides an effective solution for promoting the practical deployment of cry detection models across different scenes.