首个面向伊朗古典音乐的音视频-乐谱对齐数据集,支持音乐研究与智能生成。
The IRMA Dataset: A Structured Audio-MIDI Corpus for Iranian Classical Music
- 构建多层级音视频-乐谱对齐数据,涵盖拉迪夫核心曲目
- 包含卡里米完整拉迪夫、米尔扎·阿卜杜勒拉的乐谱与音频实例
- 适合民族音乐学、音乐生成及文化传承研究者使用
本文介绍IRMA数据集(伊朗拉迪夫MIDI音频),一个面向伊朗古典音乐计算研究的多层级开放数据集,聚焦于拉迪夫——一种结构化模态旋律单元体系,是教学与表演的核心。数据集整合符号化MIDI表示、分句级音视频对齐、音乐学转录文档(PDF)及来自多位演奏家和学者的理论信息对比表。其构建过程包括分段标注、对齐方法与结构化标识码系统,以精确追踪音乐单元。当前版本包含卡里米的完整拉迪夫、米尔扎·阿卜杜勒拉的拉迪夫MIDI文件与元数据、戴瓦米声乐拉迪夫中由佩瓦尔与费雷尤尼转录的精选片段,以及20世纪著名歌手演奏的塔希尔装饰音音频-乐谱示例。符号与分析部分采用开放许可(CC BY-NC 4.0),部分音频与第三方转录通过唱片信息引用,供用户自行查找原素材。该数据集既可作为学术档案,也支持民族音乐学、教学、符号音频研究、文化遗产保护及自动转录、音乐生成等人工智能任务。欢迎合作反馈以持续完善并融入音乐学与机器学习流程。
原文摘要 · Abstract (English)
We present the IRMA Dataset (Iranian Radif MIDI Audio), a multi-level, open-access corpus designed for the computational study of Iranian classical music, with a particular emphasis on the radif, a structured repertoire of modal-melodic units central to pedagogy and performance. The dataset combines symbolic MIDI representations, phrase-level audio-MIDI alignment, musicological transcriptions in PDF format, and comparative tables of theoretical information curated from a range of performers and scholars. We outline the multi-phase construction process, including segment annotation, alignment methods, and a structured system of identifier codes to reference individual musical units. The current release includes the complete radif of Karimi; MIDI files and metadata from Mirza Abdollah's radif; selected segments from the vocal radif of Davami, as transcribed by Payvar and Fereyduni; and a dedicated section featuring audio-MIDI examples of tahrir ornamentation performed by prominent 20th-century vocalists. While the symbolic and analytical components are released under an open-access license (CC BY-NC 4.0), some referenced audio recordings and third-party transcriptions are cited using discographic information to enable users to locate the original materials independently, pending copyright permission. Serving both as a scholarly archive and a resource for computational analysis, this dataset supports applications in ethnomusicology, pedagogy, symbolic audio research, cultural heritage preservation, and AI-driven tasks such as automatic transcription and music generation. We welcome collaboration and feedback to support its ongoing refinement and broader integration into musicological and machine learning workflows.
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