METER2800:一种用于音乐时间签名检测的新型数据集
Jeremiah Abimbola1, Daniel Kostrzewa1, Paweł Kasprowski1
1Silesian University of Technology, Poland.
Data in brief
|December 11, 2023
概括
Meter2800数据集提供了2800个音频轨道,用于音乐信息检索 (MIR) 中的时间签名检测研究. 这个新资源提供了注释音频,功能和基线结果,以推进MIR研究.
科学领域:
- 音乐信息检索 (MIR) 是一个功能.
- 数字信号处理 数字信号处理
- 机器学习用于音频分析
背景情况:
- 现有的音频数据集缺乏足够的多样性和时间签名检测的具体重点.
- 音乐信息检索研究需要专门的数据集来推进算法能力.
研究的目的:
- 介绍Meter2800数据集,这是第一个旨在检测时间签名的全面数据集.
- 为MIR研究人员提供标准化资源,包括注释音频,功能和基线性能.
- 促进音乐自动分析和理解方面的进步.
主要方法:
- 从现有的知名数据集和其他来源编译了2800个高质量的音频轨道.
- 用元数据,节奏和精确的时间签名对音频文件进行注释.
- 实施可重复性研究的火车/测试分拆战略.
- 通过四个机器学习分类实验 (SVM,KNN,Naive Bayes,Random Forest) 的技术验证.
主要成果:
- Meter2800数据集包括2.26GB的注释音频数据.
- 提供了使用各种机器学习算法检测时间签名的基线结果.
- 数据集证明了培训和测试时间签名检测模型的可行性.
结论:
- Meter2800数据集是音乐信息检索社区的一个有价值的,免费的资源.
- 它解决了以前数据集的局限性,并特别针对时间签名检测.
- 这一数据集预计将对MIR研究在节律分析方面的进展作出重大贡献.
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