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.

本文探讨了自我监督学习（SSL）在分析超过一千名新生儿的临床指示下的呜 cries 数据库方面的应用，其中包括神经损伤的 cry 检测以及疼痛、饥饿和不适的 cry 触发器的识别。通过使用大量的无标签音频数据进行预训练和 SSL 对比损失（SimCLR）进行预训练，我们表明其在神经损伤和 cry 触发器方面都比监督式预训练有更好的表现。此外，我们还通过使用未标记的婴儿 cry 进行基于 SSL 的域自适应，进一步提高了性能，并减少了整个系统所需的标记数据。