  The importance of automated and objective monitoring of dietary behavior is
becoming increasingly accepted. The advancements in sensor technology along
with recent achievements in machine-learning--based signal-processing
algorithms have enabled the development of dietary monitoring solutions that
yield highly accurate results. A common bottleneck for developing and training
machine learning algorithms is obtaining labeled data for training supervised
algorithms, and in particular ground truth annotations. Manual ground truth
annotation is laborious, cumbersome, can sometimes introduce errors, and is
sometimes impossible in free-living data collection. As a result, there is a
need to decrease the labeled data required for training. Additionally,
unlabeled data, gathered in-the-wild from existing wearables (such as Bluetooth
earbuds) can be used to train and fine-tune eating-detection models. In this
work, we focus on training a feature extractor for audio signals captured by an
in-ear microphone for the task of eating detection in a self-supervised way. We
base our approach on the SimCLR method for image classification, proposed by
Chen et al. from the domain of computer vision. Results are promising as our
self-supervised method achieves similar results to supervised training
alternatives, and its overall effectiveness is comparable to current
state-of-the-art methods. Code is available at
https://github.com/mug-auth/ssl-chewing .
