Infant Cry Detection Using Causal Temporal Representation

Identifying relevant audio features in domestic Caring for newborns, especially for first-time parents, is a environments is challenging due to diverse background sounds significant challenge. One of the main difficulties is understanding and the limited availability of high-quality annotated data the meaning of infant cries. In response, numerous for specific cases like baby cries. We address this issue studies have emerged to address this problem. Early research through manual annotation and data augmentation techniques, showed that trained adult listeners could differentiate between improving baby cry analysis models by reducing noise during types of cries. For example, [1] first identified four types of cry interval extraction. In addition, as the acquisition of cries (pain, hunger, birth, and pleasure) by training nurses annotated data is both costly and challenging, we propose a to recognize them. However, at best, the accuracy of trained viable alternative using unsupervised methods to detect infant nurses is only up to 33.09%. Beyond recognizing infants' daily cry segment boundaries by approximating the underlying needs, disease prediction is another critical task in infant cry data-generating process.