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image
imagewidth (px)
640
640
label
class label
12 classes
8pop
8pop
5jazz
1classical
8pop
0afro
6latin
1classical
11rock
4electro
5jazz
4electro
11rock
0afro
9rap
6latin
0afro
6latin
9rap
4electro
2country
8pop
2country
7metal
6latin
7metal
8pop
4electro
2country
3disco
9rap
6latin
3disco
7metal
0afro
6latin
3disco
8pop
6latin
4electro
2country
9rap
8pop
0afro
0afro
4electro
2country
6latin
4electro
5jazz
6latin
6latin
1classical
1classical
11rock
5jazz
0afro
7metal
0afro
8pop
7metal
3disco
5jazz
6latin
6latin
11rock
0afro
0afro
6latin
2country
6latin
7metal
4electro
9rap
11rock
10reggae
7metal
0afro
11rock
4electro
3disco
0afro
8pop
8pop
0afro
3disco
9rap
0afro
8pop
11rock
8pop
7metal
9rap
11rock
0afro
3disco
9rap
6latin
11rock
11rock

Dataset Card

The egtzan_plus dataset is an GTZAN like dataset for musical genre classification in the vision domain. In egtzan_plus, new classes such as Electro and Afro have been added to the original GTZAN dataset. Each audio track (30s) is transformed into a Mel-frequency spectrogram using Librosa:

# Mel-frequency spectrogram generation
y, sr = librosa.load(audio_file)
ms = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=128, fmax=8000)
log_ms = librosa.power_to_db(ms, ref=np.max)
librosa.display.specshow(log_ms)

The dataset contains the following classes:

  • Afro
  • Classical
  • Country
  • Disco
  • Electro
  • Jazz
  • Latin
  • Metal
  • Pop
  • Rap
  • Reggae
  • Rock

The dataset is split into train and test sets as follows:

  • Train: 1697 examples
  • Test: 189 examples
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Models trained or fine-tuned on ghermoso/egtzan_plus