MusAV Dataset

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.MusAV是一款全新的公开基准数据集，用于针对基于音频的音乐情感识别任务中的唤醒度（arousal）与效价（valence）回归模型开展对比验证。我们通过从Spotify应用程序接口（Spotify API）获取音乐曲目音频预览片段与元数据，对成对音乐曲目进行唤醒度与效价的对比标注，以此构建了MusAV数据集。所得数据集包含2092条曲目预览片段，涵盖1404种音乐流派，包含20名标注者给出的成对相对唤醒度与效价判断结果，以及基于不同标注一致程度的多种真值子集。 本代码仓库包含该数据集的元数据、音频曲目预览片段，以及从Spotify API获取的标注片段元数据。如需了解该数据集的使用方法与评估脚本，请参阅配套网站及相关GitHub代码仓库。 引用说明 若您使用MusAV数据集，请引用我们发表于2022年国际音乐信息检索学会会议（ISMIR 2022）的论文： Bogdanov, D., Lizarraga-Seijas, X., Alonso-Jiménez, P., & Serra, X. (2022). MusAV: 用于音频模型验证的相对唤醒度-效价标注数据集. 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" } 致谢 本研究依托项目「Musical AI - PID2019-111403GB-I00/AEI/10.13039/501100011033」开展，该项目由西班牙科学与创新部与西班牙国家研究局资助。 我们感谢所有参与本数据集标注工作的标注者。