  Every year, 3 million newborns die within the first month of life. Birth
asphyxia and other breathing-related conditions are a leading cause of
mortality during the neonatal phase. Current diagnostic methods are too
sophisticated in terms of equipment, required expertise, and general logistics.
Consequently, early detection of asphyxia in newborns is very difficult in many
parts of the world, especially in resource-poor settings. We are developing a
machine learning system, dubbed Ubenwa, which enables diagnosis of asphyxia
through automated analysis of the infant cry. Deployed via smartphone and
wearable technology, Ubenwa will drastically reduce the time, cost and skill
required to make accurate and potentially life-saving diagnoses.
