speaker_id
stringclasses
48 values
audio
audioduration (s)
0.29
1
digit
class label
10 classes
gender
class label
2 classes
accent
stringclasses
13 values
age
int64
22
61
native_speaker
bool
2 classes
origin
stringclasses
34 values
59
77
1female
German
31
false
Europe, Germany, Berlin
59
77
1female
German
31
false
Europe, Germany, Berlin
59
22
1female
German
31
false
Europe, Germany, Berlin
59
33
1female
German
31
false
Europe, Germany, Berlin
59
99
1female
German
31
false
Europe, Germany, Berlin
59
99
1female
German
31
false
Europe, Germany, Berlin
59
33
1female
German
31
false
Europe, Germany, Berlin
59
44
1female
German
31
false
Europe, Germany, Berlin
59
44
1female
German
31
false
Europe, Germany, Berlin
59
11
1female
German
31
false
Europe, Germany, Berlin
59
22
1female
German
31
false
Europe, Germany, Berlin
59
22
1female
German
31
false
Europe, Germany, Berlin
59
99
1female
German
31
false
Europe, Germany, Berlin
59
88
1female
German
31
false
Europe, Germany, Berlin
59
88
1female
German
31
false
Europe, Germany, Berlin
59
55
1female
German
31
false
Europe, Germany, Berlin
59
55
1female
German
31
false
Europe, Germany, Berlin
59
88
1female
German
31
false
Europe, Germany, Berlin
59
99
1female
German
31
false
Europe, Germany, Berlin
59
88
1female
German
31
false
Europe, Germany, Berlin
59
22
1female
German
31
false
Europe, Germany, Berlin
59
22
1female
German
31
false
Europe, Germany, Berlin
59
11
1female
German
31
false
Europe, Germany, Berlin
59
44
1female
German
31
false
Europe, Germany, Berlin
59
44
1female
German
31
false
Europe, Germany, Berlin
59
33
1female
German
31
false
Europe, Germany, Berlin
59
99
1female
German
31
false
Europe, Germany, Berlin
59
99
1female
German
31
false
Europe, Germany, Berlin
59
33
1female
German
31
false
Europe, Germany, Berlin
59
77
1female
German
31
false
Europe, Germany, Berlin
59
22
1female
German
31
false
Europe, Germany, Berlin
59
77
1female
German
31
false
Europe, Germany, Berlin
59
33
1female
German
31
false
Europe, Germany, Berlin
59
22
1female
German
31
false
Europe, Germany, Berlin
59
77
1female
German
31
false
Europe, Germany, Berlin
59
99
1female
German
31
false
Europe, Germany, Berlin
59
77
1female
German
31
false
Europe, Germany, Berlin
59
66
1female
German
31
false
Europe, Germany, Berlin
59
99
1female
German
31
false
Europe, Germany, Berlin
59
33
1female
German
31
false
Europe, Germany, Berlin
59
44
1female
German
31
false
Europe, Germany, Berlin
59
44
1female
German
31
false
Europe, Germany, Berlin
59
11
1female
German
31
false
Europe, Germany, Berlin
59
11
1female
German
31
false
Europe, Germany, Berlin
59
66
1female
German
31
false
Europe, Germany, Berlin
59
22
1female
German
31
false
Europe, Germany, Berlin
59
22
1female
German
31
false
Europe, Germany, Berlin
59
88
1female
German
31
false
Europe, Germany, Berlin
59
22
1female
German
31
false
Europe, Germany, Berlin
59
88
1female
German
31
false
Europe, Germany, Berlin
59
99
1female
German
31
false
Europe, Germany, Berlin
59
55
1female
German
31
false
Europe, Germany, Berlin
59
55
1female
German
31
false
Europe, Germany, Berlin
59
88
1female
German
31
false
Europe, Germany, Berlin
59
22
1female
German
31
false
Europe, Germany, Berlin
59
99
1female
German
31
false
Europe, Germany, Berlin
59
88
1female
German
31
false
Europe, Germany, Berlin
59
22
1female
German
31
false
Europe, Germany, Berlin
59
22
1female
German
31
false
Europe, Germany, Berlin
59
66
1female
German
31
false
Europe, Germany, Berlin
59
11
1female
German
31
false
Europe, Germany, Berlin
59
11
1female
German
31
false
Europe, Germany, Berlin
59
44
1female
German
31
false
Europe, Germany, Berlin
59
44
1female
German
31
false
Europe, Germany, Berlin
59
33
1female
German
31
false
Europe, Germany, Berlin
59
99
1female
German
31
false
Europe, Germany, Berlin
59
66
1female
German
31
false
Europe, Germany, Berlin
59
99
1female
German
31
false
Europe, Germany, Berlin
59
77
1female
German
31
false
Europe, Germany, Berlin
59
22
1female
German
31
false
Europe, Germany, Berlin
59
77
1female
German
31
false
Europe, Germany, Berlin
59
33
1female
German
31
false
Europe, Germany, Berlin
59
33
1female
German
31
false
Europe, Germany, Berlin
59
77
1female
German
31
false
Europe, Germany, Berlin
59
22
1female
German
31
false
Europe, Germany, Berlin
59
99
1female
German
31
false
Europe, Germany, Berlin
59
33
1female
German
31
false
Europe, Germany, Berlin
59
99
1female
German
31
false
Europe, Germany, Berlin
59
99
1female
German
31
false
Europe, Germany, Berlin
59
44
1female
German
31
false
Europe, Germany, Berlin
59
44
1female
German
31
false
Europe, Germany, Berlin
59
11
1female
German
31
false
Europe, Germany, Berlin
59
11
1female
German
31
false
Europe, Germany, Berlin
59
88
1female
German
31
false
Europe, Germany, Berlin
59
99
1female
German
31
false
Europe, Germany, Berlin
59
88
1female
German
31
false
Europe, Germany, Berlin
59
22
1female
German
31
false
Europe, Germany, Berlin
59
22
1female
German
31
false
Europe, Germany, Berlin
59
55
1female
German
31
false
Europe, Germany, Berlin
59
55
1female
German
31
false
Europe, Germany, Berlin
59
22
1female
German
31
false
Europe, Germany, Berlin
59
22
1female
German
31
false
Europe, Germany, Berlin
59
99
1female
German
31
false
Europe, Germany, Berlin
59
88
1female
German
31
false
Europe, Germany, Berlin
59
88
1female
German
31
false
Europe, Germany, Berlin
59
11
1female
German
31
false
Europe, Germany, Berlin
59
11
1female
German
31
false
Europe, Germany, Berlin
59
44
1female
German
31
false
Europe, Germany, Berlin
59
44
1female
German
31
false
Europe, Germany, Berlin
59
99
1female
German
31
false
Europe, Germany, Berlin

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},
}
Downloads last month
0
Edit dataset card