Published October 16, 2024 | Version 1.1.0
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MERGE Dataset

  • 1. Centre for Informatics and Systems of the University of Coimbra
  • 2. Polytechnic Institute of Leiria - School of Technology and Management
  • 3. Ci2 - Smart Cities Research Centre - Polytechnic Institute of Tomar

Description

The MERGE dataset is a collection of audio, lyrics, and bimodal datasets for conducting research on Music Emotion Recognition. A complete version is provided for each modality. The audio datasets provide 30-second excerpts for each sample, while full lyrics are provided in the relevant datasets. The amount of available samples in each dataset is the following:

  • MERGE Audio Complete: 3554
  • MERGE Audio Balanced: 3232
  • MERGE Lyrics Complete: 2568
  • MERGE Lyrics Balanced: 2400
  • MERGE Bimodal Complete: 2216
  • MERGE Bimodal Balanced: 2000

Additional Contents

Each dataset contains the following additional files:

  • av_values: File containing the arousal and valence values for each sample sorted by their identifier;
  • tvt_dataframes: Train, validate, and test splits for each dataset. Both a 70-15-15 and a 40-30-30 split are provided.

Metadata

A metadata spreadsheet is provided for each dataset with the following information for each sample, if available:

  • Song (Audio and Lyrics datasets) - Song identifiers. Identifiers starting with MT were extracted from the AllMusic platform, while those starting with A or L were collected from private collections;
  • Quadrant - Label corresponding to one of the four quadrants from Russell's Circumplex Model;
  • AllMusic Id -  For samples starting with A or L, the matching AllMusic identifier is also provided. This was used to complement the available information for the samples originally obtained from the platform;
  • Artist - First performing artist or band;
  • Title - Song title;
  • Relevance - AllMusic metric representing the relevance of the song in relation to the query used;
  • Duration - Song length in seconds;
  • Moods - User-generated mood tags extracted from the AllMusic platform and available in Warriner's affective dictionary;
  • MoodsAll - User-generated mood tags extracted from the AllMusic platform;
  • Genres - User-generated genre tags extracted from the AllMusic platform;
  • Themes - User-generated theme tags extracted from the AllMusic platform;
  • Styles - User-generated style tags extracted from the AllMusic platform;
  • AppearancesTrackIDs - All AllMusic identifiers related with a sample;
  • Sample - Availability of the sample in the AllMusic platform;
  • SampleURL - URL to the 30-second excerpt in AllMusic;
  • ActualYear - Year of song release.

Citation

If you use some part of the MERGE dataset in your research, please cite the following article:

Louro, P. L. and Redinho, H. and Santos, R. and Malheiro, R. and Panda, R. and Paiva, R. P. (2024). MERGE - A Bimodal Dataset For Static Music Emotion Recognition. arxiv. URL: https://arxiv.org/abs/2407.06060.

BibTeX:

@misc{louro2024mergebimodaldataset,
      title={MERGE -- A Bimodal Dataset for Static Music Emotion Recognition}, 
      author={Pedro Lima Louro and Hugo Redinho and Ricardo Santos and Ricardo Malheiro and Renato Panda and Rui Pedro Paiva},
      year={2024},
      eprint={2407.06060},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2407.06060}, 
}

Acknowledgements

This work is funded by FCT - Foundation for Science and Technology, I.P., within the scope of the projects: MERGE - DOI: 10.54499/PTDC/CCI-COM/3171/2021 financed with national funds (PIDDAC) via the Portuguese State Budget; and project CISUC - UID/CEC/00326/2020 with funds from the European Social Fund, through the Regional Operational Program Centro 2020.

Renato Panda was supported by Ci2 - FCT UIDP/05567/2020.

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Additional details

Related works

Is described by
Preprint: arXiv:2407.06060 (arXiv)

Software

Development Status
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