卡登萨歌词可理解性预测 (CLIP) 数据集
Gerardo Roa-Dabike1, Trevor J Cox2, Jon P Barker1
1School of Computer Science, University of Sheffield, UK.
Data in brief
|February 3, 2026
概括
本研究介绍了CLIP,这是一套独特的音乐信息检索 (MIR) 研究数据集,包含流行西方音乐,歌词和可理解性分数. 它有助于开发机器学习模型来预测歌词可理解性.
科学领域:
- 音乐信息检索 (MIR) 是一个功能.
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 现有的数据集缺乏MIR的全面歌词可理解性数据.
- 开发算法来预测歌词可理解性对于各种应用来说至关重要.
研究的目的:
- 介绍CLIP数据集,这是MIR研究的大规模资源.
- 促进机器学习模型的开发,以预测歌词的可理解性.
- 支持卡登扎ICASSP 2026信号处理大挑战.
主要方法:
- 编译了来自独立艺术家的11,072个西方音乐信号 (免费音乐档案).
- 通过英语母语使用者生成地面真相歌词.
- 模拟听力损失 (没有,轻度,中度) 创建11100个音频信号.
- 通过在线听力实验收集人类的转录来确定可理解性得分.
主要成果:
- CLIP数据集包括11100个音乐信号的音频,基本真相歌词和可理解性分数.
- 它是首个公开可用的大型数据集,用于歌词可理解性预测.
- 数据集代表了各种听力状况.
结论:
- CLIP数据集为推进MIR研究提供了宝贵的资源,特别是在歌词可理解性方面.
- 它可以创建更强大,更准确的歌词可理解性预测模型.
- 这一数据集将促进音乐信号处理和人机交互方面的创新.
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