metadata

language:
  - en
license: mit
size_categories:
  - 10K<n<100K
task_categories:
  - audio-classification
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
dataset_info:
  features:
    - name: speaker_id
      dtype: string
    - name: audio
      dtype:
        audio:
          sampling_rate: 16000
    - name: digit
      dtype:
        class_label:
          names:
            '0': '0'
            '1': '1'
            '2': '2'
            '3': '3'
            '4': '4'
            '5': '5'
            '6': '6'
            '7': '7'
            '8': '8'
            '9': '9'
    - name: gender
      dtype:
        class_label:
          names:
            '0': male
            '1': female
    - name: accent
      dtype: string
    - name: age
      dtype: int64
    - name: native_speaker
      dtype: bool
    - name: origin
      dtype: string
  splits:
    - name: train
      num_bytes: 1493209727
      num_examples: 24000
    - name: test
      num_bytes: 360966680
      num_examples: 6000
  download_size: 1483680961
  dataset_size: 1854176407

Dataset Card for "AudioMNIST"

The audioMNIST dataset has 50 English recordings per digit (0-9) of 60 speakers. There are 60 participants in total, with 12 being women and 48 being men, all featuring a diverse range of accents and country of origin. Their ages vary from 22 to 61 years old. This is a great dataset to explore a simple audio classification problem: either the digit or the gender.

Bias, Risks, and Limitations

The genders represented in the dataset are unbalanced, with around 80% being men.
The majority of the speakers, around 70%, have a German accent

Citation Information

The original creators of the dataset ask you to cite their paper if you use this data:

@ARTICLE{becker2018interpreting,
  author    = {Becker, S\"oren and Ackermann, Marcel and Lapuschkin, Sebastian and M\"uller, Klaus-Robert and Samek, Wojciech},
  title     = {Interpreting and Explaining Deep Neural Networks for Classification of Audio Signals},
  journal   = {CoRR},
  volume    = {abs/1807.03418},
  year      = {2018},
  archivePrefix = {arXiv},
  eprint    = {1807.03418},
}