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.

翻译：本文探索利用自监督学习（SSL）分析首个包含千余名新生儿临床指征的哭声录音数据库。具体而言，我们针对基于哭声的神经损伤检测以及疼痛、饥饿、不适等哭声诱因识别展开研究。在医疗环境中标注大型数据库既昂贵又耗时，通常需要多位专家经年累月的协作。利用大量无标注音频数据学习有效表征，能够降低构建鲁棒模型乃至最终临床解决方案的成本。本研究在大型音频数据集上实验了卷积神经网络的自监督预训练方法。研究表明，采用SSL对比损失（SimCLR）进行预训练在神经损伤和哭声诱因识别任务中均显著优于监督预训练。此外，我们通过利用无标注婴儿哭声数据开展基于SSL的领域自适应，进一步提升了模型性能。实验证明，此类基于SSL的预训练方法用于哭声声音自适应时，能够降低整个系统对标注数据的需求量。