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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***AudioMNIST*** consists of audio recordings of 60 different speakers saying the digits 0 to 9, with 50 recordings per digit per speaker [1, 2]. The processed dataset contains 30,000 (univariate) time series, each of length 47,998 (approximately 1 second of data sampled at 44khz), with ten classes representing the digits 0 to 9. This version of the dataset has been split into cross-validation folds based on speaker (i.e., such that recordings for a given speaker do not appear in both the training and validation sets). ***AudioMNIST-DS*** is a variant of the same dataset downsampled to a length of 4,000.
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[1] Sören Becker, Johanna Vielhaben, Marcel Ackermann, Klaus-Robert Müller, Sebastian Lapuschkin, and Wojciech Samek. (2024). AudioMNIST: Exploring explainable artificial intelligence for audio analysis on a simple benchmark. *Journal of the Franklin Institute*, 361(1):418–428.
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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: mit
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pretty_name: AudioMNIST-DS
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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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|AudioMNIST-DS||
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|Category|Audio|
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|Num. Examples|30,000|
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|Num. Channels|1|
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|Length|4,000|
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|Sampling Freq.|~4 kHz|
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|Num. Classes|10|
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|License|[MIT](https://opensource.org/license/mit)|
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|Citations|[1] [2]|
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***AudioMNIST*** consists of audio recordings of 60 different speakers saying the digits 0 to 9, with 50 recordings per digit per speaker [1, 2]. The processed dataset contains 30,000 (univariate) time series, each of length 47,998 (approximately 1 second of data sampled at 44khz), with ten classes representing the digits 0 to 9. This version of the dataset has been split into cross-validation folds based on speaker (i.e., such that recordings for a given speaker do not appear in both the training and validation sets). ***AudioMNIST-DS*** is a variant of the same dataset downsampled to a length of 4,000.
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[1] Sören Becker, Johanna Vielhaben, Marcel Ackermann, Klaus-Robert Müller, Sebastian Lapuschkin, and Wojciech Samek. (2024). AudioMNIST: Exploring explainable artificial intelligence for audio analysis on a simple benchmark. *Journal of the Franklin Institute*, 361(1):418–428.
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