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Part of MONSTER: https://arxiv.org/abs/2502.15122.
MosquitoSound, taken from the broader UCR archive, consists of 279,566 (univariate) time series, each of length 3,750, representing recordings of wingbeats for six different species of mosquito [1, 2]. The task is to identify the species of mosquito based on the recordings. This version of the dataset has been split into stratified random cross-validation folds.
[1] Eleftherios Fanioudakis, Matthias Geismar, and Ilyas Potamitis. (2018). Mosquito wingbeat analysis and classification using deep learning. In 26th European Signal Processing Conference, pages 2410–2414.
[2] Ilyas Potamitis. (2018). Wingbeats. https://www.kaggle.com/datasets/potamitis/wingbeats; https://timeseriesclassification.com/description.php?Dataset=MosquitoSound. Public Domain.
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