  In this paper, we explore self-supervised learning (SSL) for analyzing a
first-of-its-kind database of cry recordings containing clinical indications of
more than a thousand newborns. Specifically, we target cry-based detection of
neurological injury as well as identification of cry triggers such as pain,
hunger, and discomfort. Annotating a large database in the medical setting is
expensive and time-consuming, typically requiring the collaboration of several
experts over years. Leveraging large amounts of unlabeled audio data to learn
useful representations can lower the cost of building robust models and,
ultimately, clinical solutions. In this work, we experiment with
self-supervised pre-training of a convolutional neural network on large audio
datasets. We show that pre-training with SSL contrastive loss (SimCLR) performs
significantly better than supervised pre-training for both neuro injury and cry
triggers. In addition, we demonstrate further performance gains through
SSL-based domain adaptation using unlabeled infant cries. We also show that
using such SSL-based pre-training for adaptation to cry sounds decreases the
need for labeled data of the overall system.
