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tags:
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- audio
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---
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Part of MONSTER: <https://arxiv.org/abs/2502.15122>.
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***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.
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[1] Eleftherios Fanioudakis, Matthias Geismar, and Ilyas Potamitis. (2018). Mosquito wingbeat analysis and classification using deep learning. In *26<sup>th</sup> European Signal Processing Conference*, pages 2410–2414.
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---
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tags:
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- time series
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- time series classification
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- monster
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- audio
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license: other
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pretty_name: MosquitoSound
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size_categories:
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- 100K<n<1M
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---
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Part of MONSTER: <https://arxiv.org/abs/2502.15122>.
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|MosquitoSound||
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|Category|Audio|
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|Num. Examples|279,566|
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|Num. Channels|1|
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|Length|3,750|
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|Sampling Freq.|6 kHz|
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|Num. Classes|1|
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|License|Public Domain|
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|Citations|[1] [2]|
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***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.
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[1] Eleftherios Fanioudakis, Matthias Geismar, and Ilyas Potamitis. (2018). Mosquito wingbeat analysis and classification using deep learning. In *26<sup>th</sup> European Signal Processing Conference*, pages 2410–2414.
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