Datasets:
File size: 2,237 Bytes
418456c fb5980b 418456c fb5980b 418456c fa037ad 418456c fa037ad 418456c fa037ad 418456c fa037ad 418456c b088f1a 3afdf21 b088f1a 418456c 7b59387 77f00dd 7b59387 77f00dd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 |
---
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},
}
``` |