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The importance of automated and objective monitoring of dietary behavior isbecoming increasingly accepted. The advancements in sensor technology alongwith recent achievements in machine-learning--based signal-processingalgorithms have enabled the development of dietary monitoring solutions thatyield highly accurate results. A common bottleneck for developing and trainingmachine learning algorithms is obtaining labeled data for training supervisedalgorithms, and in particular ground truth annotations. Manual ground truthannotation is laborious, cumbersome, can sometimes introduce errors, and issometimes impossible in free-living data collection. As a result, there is aneed to decrease the labeled data required for training. Additionally,unlabeled data, gathered in-the-wild from existing wearables (such as Bluetoothearbuds) can be used to train and fine-tune eating-detection models. In thiswork, we focus on training a feature extractor for audio signals captured by anin-ear microphone for the task of eating detection in a self-supervised way. Webase our approach on the SimCLR method for image classification, proposed byChen et al. from the domain of computer vision. Results are promising as ourself-supervised method achieves similar results to supervised trainingalternatives, and its overall effectiveness is comparable to currentstate-of-the-art methods. Code is available athttps://github.com/mug-auth/ssl-chewing .

Self-Supervised Feature Learning of 1D Convolutional Neural Networks with Contrastive Loss for Eating Detection Using an In-Ear Microphone