Datasets:
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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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***InsectSound***, taken from the broader UCR archive, consists of 50,000 (univariate) time series, each of length 600, representing recordings of wingbeats for six species of insects, with 2 different genders for 4 of the 6 species [1, 2]. This version of the dataset has been split into stratified random cross-validation folds.
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[1] Yanping Chen, Adena Why, Gustavo Batista, Agenor Mafra-Neto, and Eamonn Keogh. (2014). Flying insect classification with inexpensive sensors. *Journal of Insect Behavior*, 27(5):657–677.
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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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- monst
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- audio
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license: other
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pretty_name: InsectSound
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size_categories:
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- 10K<n<100K
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---
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Part of MONSTER: <https://arxiv.org/abs/2502.15122>.
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|InsectSound||
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|Category|Audio|
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|Num. Examples|50,000|
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|Num. Channels|1|
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|Length|600|
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|Sampling Freq.|6 kHz|
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|Num. Classes|10|
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|License|Public Domain|
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|Citations|[1] [2]|
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***InsectSound***, taken from the broader UCR archive, consists of 50,000 (univariate) time series, each of length 600, representing recordings of wingbeats for six species of insects, with 2 different genders for 4 of the 6 species [1, 2]. This version of the dataset has been split into stratified random cross-validation folds.
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[1] Yanping Chen, Adena Why, Gustavo Batista, Agenor Mafra-Neto, and Eamonn Keogh. (2014). Flying insect classification with inexpensive sensors. *Journal of Insect Behavior*, 27(5):657–677.
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