Published October 31, 2023 | Version v1
Dataset Open

STraDa: A Singer Traits Dataset

  • 1. Deezer
  • 2. ROR icon École Centrale de Nantes
  • 3. ROR icon Laboratoire des Sciences du Numérique de Nantes

Contributors

  • 1. Deezer Research

Description

What is STraDa?

STraDa is a dataset that was presented at the late breaking demo session of ISMIR 2023. The detailed description of the dataset is in README.md.

STraDa is large-scale music audio dataset that contains singers' metadata, tracks' metadata, IDs for downloading audios of 30s (preview parts) by using Deezer API. This dataset could be used for various MIR tasks, such as singer identification, singer recognition, singer gender/age detection, genre classification, language classification. The training set contains 25194 excerpts of 30s, and 5264 singers. The testing set contains 200 songs from 200 singers that are balanced across two genders, 5 languages and 4 age groups (5 song/gender/language/age group), that could be used for bias analysis.

 

What does STraDa contain?

An important feature of STraDa is that each track only has a single lead singer, which improves the accuracy of annotations.

The annotations in the training set are gathered and cross-validated from 4 different data sources: Deezer, Wikidata, musicbrainz, discogs

The testing set is curated and annotated manually to ensure perfect accuracy.

Singers' metadata contains gender, birth year and active country. Tracks' metadata contains genre, language and release date.

 

What could STraDa be used for?

STraDa could be used for singer identification, singer recognition, singer gender/age detection, song genre/language identification. The balance in the testing set could enable bias analysis.

 

Dataset use

This dataset is only available for conducting non-commercial research related to audio analysis under license Creative Commons Attribution Non Commercial 2.5 Generic. It's important to note that data under this license are data contained in STraDa, not applicable to audios. We do NOT grant permission for any modification, generation or manipulation using these audios.

We wholeheartedly welcome researchers to use STraDa for their own research purpose. Please send an email to ykong@deezer.com if you have any questions about the data.

Citation

If you use STraDa, please cite following paper:

@inproceedings{kong2024stradasingertraitsdataset,
  title={STraDa: A Singer Traits Dataset},
  author={Yuexuan Kong and Viet-Anh Tran and Romain Hennequin},
  booktitle={Interspeech 2024},
  year={2024}
}

Files

artist_info.csv

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md5:ddd555b351742f35053cee7a55484b88
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Additional details

Related works

Is metadata for
Conference proceeding: 10.48550/arXiv.2406.04140 (DOI)