标题： IRMA数据集：伊朗古典音乐的结构化音频-MIDI语料库 显示英文标题

标题： The IRMA Dataset: A Structured Audio-MIDI Corpus for Iranian Classical Music

摘要： 我们介绍了IRMA数据集（伊朗拉迪夫MIDI音频），这是一个多层次的开放访问语料库，旨在促进对伊朗古典音乐的计算研究，特别关注拉迪夫，这是一种结构化的调式旋律单元曲目，是教学和表演的核心。 该数据集结合了符号MIDI表示、乐句级音频-MIDI对齐、以PDF格式呈现的音乐学转录文本，以及从多位表演者和学者处整理的理论信息比较表格。 我们概述了多阶段的构建过程，包括片段注释、对齐方法以及用于引用单个音乐单元的结构化标识符系统。 当前版本包括Karimi的完整拉迪夫；Mirza Abdollah拉迪夫的MIDI文件和元数据；Davami的声乐拉迪夫片段，由Payvar和Fereyduni转录；以及一个专门部分，包含20世纪著名歌手演奏的tahrir装饰音的音频-MIDI示例。 尽管符号和分析组件以开放访问许可（CC BY-NC 4.0）发布，但一些参考的音频录音和第三方转录文本使用唱片信息进行引用，以使用户能够独立查找原始材料，待版权许可批准后。 作为学术档案和计算分析资源，该数据集支持民族音乐学、教学法、符号音频研究、文化遗产品保护以及自动转录和音乐生成等AI驱动任务的应用。 我们欢迎合作与反馈，以支持其持续改进，并更广泛地整合到音乐学和机器学习工作流程中。 显示英文摘要

摘要： 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.