MusAV Dataset
- 1. Music Technology Group, Universitat Pompeu Fabra
Description
MusAV is a new public benchmark dataset for comparative validation of arousal and valence (AV) regression models for audio-based music emotion recognition. We built MusAV by gathering comparative annotations of arousal and valence on pairs of music tracks, using track audio previews and metadata from the Spotify API. The resulting dataset contains 2,092 track previews covering 1,404 genres, with pairwise relative AV judgments by 20 annotators and various subsets of the ground truth based on different levels of annotation agreement.
This repository contains the dataset metadata, audio track previews and metadata gathered from the Spotify API for the annotated chunks. Please see the companion website and the related GitHub repository for more information on how to use the dataset and evaluation scripts.
Citation
If you use the MusAV Dataset, please cite our ISMIR 2022 paper:
Bogdanov, D., Lizarraga-Seijas, X., Alonso-Jiménez, P., & Serra X. (2022). MusAV: A dataset of relative arousal-valence annotations for validation of audio models. International Society for Music Information Retrieval Conference (ISMIR 2022).
BibTeX:
@conference {bogdanov2019mtg,
author = "Bogdanov, Dmitry and Lizarraga-Seijas, Xavier and Alonso-Jiménez, Pablo and Serra, Xavier",
title = "MusAV: A dataset of relative arousal-valence annotations for validation of audio models",
booktitle = "International Society for Music Information Retrieval Conference (ISMIR 2022)",
year = "2022",
address = "Bengaluru, India",
url = "http://hdl.handle.net/10230/54181"
}
Acknowledgments
This research was carried out under the project Musical AI - PID2019-111403GB-I00/AEI/10.13039/501100011033, funded by the Spanish Ministerio de Ciencia e Innovación and the Agencia Estatal de Investigación.
We thank all the annotators who participated in the creation of the dataset.
Files
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Please include in the justification field your academic affiliation (if you have one) and a brief description of your research topics and why you would like to use this dataset. If you do not include this information we may not approve your request.
The audio track previews and metadata gathered from the Spotify API for the MusAV dataset are released for non-commercial scientific research purposes only. Any publication of results based on the data extracts of the Spotify database must cite Spotify as the source of the data.
The MusAV dataset is offered free of charge for internal non-commercial use only. You may not redistribute, publically communicate or modify it, unless expressly permitted by the Universitat Pompeu Fabra (UPF) or by applicable law. The individual contents of the dataset may be protected by copyright, and a non-transferrable limited license to reproduce and use the same is granted for the indicated purpose only.
The dataset and its contents are made available on an “as is” basis and without warranties of any kind, including without limitation satisfactory quality and conformity, merchantability, fitness for a particular purpose, accuracy or completeness, or absence of errors. Subject to any liability that may not be excluded or limited by law, the UPF is not liable for, and expressly excludes, all liability for loss or damage however and whenever caused to anyone by any use of the MusAV dataset or any part of it.
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