Civit M, Drai-Zerbib V, Lizcano D, Escalona M J
Department of Communication and Education, Universidad Loyola Andalucía. Av. de las Universidades s/n. 41704 Sevilla, Spain.
LEAD - CNRS UMR5022 Université Bourgogne Institut Marey - I3M, 64 rue de Sully, Dijon 21000, France.
Data Brief. 2024 Jul 18;55:110743. doi: 10.1016/j.dib.2024.110743. eCollection 2024 Aug.
The SunoCaps dataset aims to provide an innovative contribution to music data. Expert description of human-made musical pieces, from the widely used MusicCaps dataset, are used as prompts for generating complete songs for this dataset. This Automatic Music Generation is done with the state-of-the-art Suno generator of audio-based music. A subset of 64 pieces from MusicCaps is currently included, with a total of 256 generated entries. This total stems from generating four different variations for each human piece; two versions based on the original caption and two versions based on the original aspect description. As an AI-generated music dataset, SunoCaps also includes expert-based information on prompt alignment, with the main differences between prompt and final generation annotated. Furthermore, annotations describing the main discrete emotions induced by the piece. This dataset can have an array of implementations, such as creating and improving music generation validation tools, training systems for multi-layered architectures and the optimization of music emotion estimation systems.
SunoCaps数据集旨在为音乐数据做出创新性贡献。来自广泛使用的MusicCaps数据集中人工制作音乐作品的专家描述，被用作生成该数据集完整歌曲的提示。这种自动音乐生成是使用基于音频的音乐的最先进的Suno生成器完成的。目前包含来自MusicCaps的64首作品的一个子集，共有256个生成的条目。这个总数来自为每首人工作品生成四种不同的变体；两个基于原始标题的版本和两个基于原始方面描述的版本。作为一个由人工智能生成的音乐数据集，SunoCaps还包括基于专家的提示对齐信息，并标注了提示与最终生成之间的主要差异。此外，还有描述作品引发的主要离散情绪的注释。该数据集可以有一系列应用，比如创建和改进音乐生成验证工具、用于多层架构的训练系统以及优化音乐情感估计系统。
