README.md

---
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: 1493209727.0
    num_examples: 24000
  - name: test
    num_bytes: 360966680.0
    num_examples: 6000
  download_size: 1483680961
  dataset_size: 1854176407.0
---
# Dataset Card for "AudioMNIST"
The [audioMNIST](https://github.com/soerenab/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](https://arxiv.org/abs/1807.03418) 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},
}
```