MERGE Dataset
Creators
- 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.
Files
MERGE_Audio_Balanced.zip
Files
(3.6 GB)
Name | Size | Download all |
---|---|---|
md5:bbca042c520fd16bca74b572ac6c88e3
|
1.1 GB | Preview Download |
md5:167f7ba80ab8eca8eee5a8668498a419
|
1.2 GB | Preview Download |
md5:df9d1483ee3aacece103f7d5665fd7ec
|
661.1 MB | Preview Download |
md5:4b4dbe24083a5987e0f37ce4eb1df771
|
739.2 MB | Preview Download |
md5:4578450254a047756cd6d20811034a71
|
2.2 MB | Preview Download |
md5:fae5bd689febeddc96595ac1843ffc77
|
2.3 MB | Preview Download |
Additional details
Related works
- Is described by
- Preprint: arXiv:2407.06060 (arXiv)
Software
- Development Status
- Active