| Publication Type |
Conference Paper |
| Authors |
David Kopyto, Rui Zhang, Oliver Amft |
| Title |
Audio-Based Onset Detection applied to Chewing Cycle Segmentation |
| 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. |
| Date |
September 21, 2021 |
| Proceedings Title |
ACM International Symposium on Wearable Computers |
| Conference Name |
ISWC '21 |
| Place |
Virtual Conference |
| Publisher |
Association for Computing Machinery |
| Pages |
124–128 |
| Series |
ISWC '21 |
| DOI |
10.1145/3460421.3478819 |
| ISBN |
978-1-4503-8462-9 |
| URL |
Publisher's website |
| Accessed |
2021-09-21 |
| Library Catalog |
ACM Digital Library |
| Full Text |
PDF |
