A dissertation showing that transfer learning, tensor-compressed RNNs, and domain adaptation improve infant-cry models, while releasing the CryCeleb dataset for cry-based infant recognition.
Ubenwa: Cry-based Diagnosis of Birth Asphyxia
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abstract
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.
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2025 1verdicts
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Making deep neural networks work for medical audio: representation, compression and domain adaptation
A dissertation showing that transfer learning, tensor-compressed RNNs, and domain adaptation improve infant-cry models, while releasing the CryCeleb dataset for cry-based infant recognition.