---
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': afro
          '1': classical
          '2': country
          '3': disco
          '4': electro
          '5': jazz
          '6': latin
          '7': metal
          '8': pop
          '9': rap
          '10': reggae
          '11': rock
  splits:
  - name: train
    num_bytes: 128963338.5857826
    num_examples: 1697
  - name: test
    num_bytes: 14256351.565217393
    num_examples: 189
  download_size: 143291941
  dataset_size: 143219690.151
license: mit
task_categories:
- image-classification
tags:
- music
size_categories:
- 1K<n<10K
---
# 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:


```python
# 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