ATDD:用于在音乐录音中自动调音检测的多语言数据集
Mahyar Gohari1, Paolo Bestagini2, Sergio Benini1
1Department of Information Engineering, CNIT - University of Brescia, Italy.
Data in brief
|February 2, 2026
概括
这项研究提出了一个新的多语言数据集,以区分自动调音乐与真实录音. 这个资源有助于开发强大的音乐分析和机器学习模型.
科学领域:
- 音乐信息检索 音乐信息检索
- 机器学习 机器学习
- 音频信号处理 音频信号处理
背景情况:
- 现有的资源缺乏足够的数据来区分自动调音乐.
- 多语言数据集对于音乐分析中的各种应用至关重要.
研究的目的:
- 介绍一套新的多语言数据集,用于自动调音乐检测.
- 为音乐分析和机器学习领域的研究人员提供了宝贵的资源.
主要方法:
- 收集了英语,普通话和日语的各种音乐数据.
- 使用概率YIN (PYIN) 和pitch-synchronized重叠和添加 (PSOLA) 进行调音校正.
- 生成时间域标签,用于精确的音调校正注释.
主要成果:
- 创建了一个全面的数据集,包括自动调和真实的音乐部分.
- 数据集包括关于音调校正应用程序的详细注释.
- 标准化的音频文件分为10秒间隔的16kHz.
结论:
- 该数据集有助于训练可靠的自动调音乐检测模型.
- 能够进行细微分析,并支持各种研究应用.
- 对于推进音乐分析和人工智能研究的宝贵资源.
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