---
dataset_info:
  features:
  - name: audio
    dtype:
      audio:
        sampling_rate: 22050
  - name: genre
    dtype: string
  - name: label
    dtype:
      class_label:
        names:
          '0': blues
          '1': classical
          '2': country
          '3': disco
          '4': hiphop
          '5': jazz
          '6': metal
          '7': pop
          '8': reggae
          '9': rock
  splits:
  - name: train
    num_bytes: 586664927
    num_examples: 443
  - name: validation
    num_bytes: 260793810
    num_examples: 197
  - name: test
    num_bytes: 383984112
    num_examples: 290
  download_size: 1230811404
  dataset_size: 1231442849
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
task_categories:
- audio-classification
tags:
- audio
- multiclass
- music
---

# GTZAN Music Genre Classification

GTZAN consists of 100 30-second recording excerpts in each of 10 categories, and is the most-used public dataset in music information retrieval (MIR) research.
Following Kereliuk et al. (2015), we use the "fault-filtered" partitioning version of GTZAN, which is constructed by hand to include 443/197/290 excerpts. 
This version of database could be found and downloaded from [here](https://www.kaggle.com/datasets/carlthome/gtzan-genre-collection).

## Citations

```bibtex
@article{kereliuk2015deep,
  title={Deep learning and music adversaries},
  author={Kereliuk, Corey and Sturm, Bob L and Larsen, Jan},
  journal={IEEE Transactions on Multimedia},
  volume={17},
  number={11},
  pages={2059--2071},
  year={2015},
  publisher={IEEE}
}
```

```bibtex
@article{sturm2014state,
  title={The state of the art ten years after a state of the art: Future research in music information retrieval},
  author={Sturm, Bob L},
  journal={Journal of new music research},
  volume={43},
  number={2},
  pages={147--172},
  year={2014},
  publisher={Taylor \& Francis}
}
```

```bibtex
@article{tzanetakis2002musical,
  title={Musical genre classification of audio signals},
  author={Tzanetakis, George and Cook, Perry},
  journal={IEEE Transactions on speech and audio processing},
  volume={10},
  number={5},
  pages={293--302},
  year={2002},
  publisher={IEEE}
}
```