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Automatic food intake monitoring using wearable systems is a promising research direction in the fight against obesity and eating disorders. Previous work has indicated progress towards automatic food intake monitoring using acoustic sensors for detecting periods of food intake, swallowing and chewing detection, and discriminating between solid and liquid food intake. However, little effort has been put towards acoustic detection in noisy recordings. In this paper, we explore detecting food intake events (particularly chew events) in the presence of restaurant background noise. Three templates were extracted from a clean signal to represent the beginning, middle and end phase of a chewing sequence. Then, each template was used with sliding window correlation to detect chew events in a noisy recording. The noisy signal was formed by instantaneous addition of a clean throat microphone recording during …