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Part of MONSTER: https://arxiv.org/abs/2502.15122.
| FruitFlies | |
|---|---|
| Category | Audio |
| Num. Examples | 34,518 |
| Num. Channels | 1 |
| Length | 5,000 |
| Sampling Freq. | 8 kHz |
| Num. Classes | 3 |
| License | Other |
| Citations | [1] [2] |
FruitFlies, taken from the broader UCR archive, consistst of 34,518 (univariate) time series, each of length 5,000, representing acoustic recordings of wingbeats for three species of fruit fly [1, 2]. The recordings are single channel with a sampling rate of 8 kHz (i.e., each recording represents just over half a second of data). The recordings are made using a specialised infrared sensor which detects the vibrations of the wings of the insects. The learning task is to identify the species of fly based on the recordings. This version of the dataset has been split into stratified random cross-validation folds.
[1] Ilyas Potamitis. (2016). FruitFlies dataset. https://timeseriesclassification.com/description.php?Dataset=FruitFlies. With Permission of Prof Tony Bagnall.
[2] Michael Flynn. (2022). Classifying Dangerous Species of Mosquito Using Machine Learning. PhD thesis, University of East Anglia, 2022.
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