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- W3200901301 abstract "In this paper we compare three onset detection algorithms for acoustic chewing cycle detection, which is a basic step in eating detection and automated dietary monitoring. We introduce a spectral flux algorithm that uses the spectrogram of a chewing sequence to compute a novelty function. Furthermore, beat tracking, in particular the notion of a predominant local pulse is introduced. We compare the two algorithms to a baseline energy-based segmentation in a chewing dataset with seven participants consuming pieces of six different foods, including in total 9818 annotated chewing cycles. Best performance was achieved for the beat tracking algorithm with 83% F-measure after leave-one-participant-out cross validation." @default.
- W3200901301 created "2021-09-27" @default.
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- W3200901301 date "2021-09-21" @default.
- W3200901301 modified "2023-10-16" @default.
- W3200901301 title "Audio-Based Onset Detection applied to Chewing Cycle Segmentation" @default.
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- W3200901301 doi "https://doi.org/10.1145/3460421.3478819" @default.
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