构建首个基于乐谱的印度古典音乐拉格数据集并用图结构建模旋律特征。
Graph Representation of RaagBase: A Unique Dataset for Hindustani Music
- 用作曲家巴特克汉德的乐谱序列构建图结构，节点为作品，边基于音符频率相似性连接。
- 图聚类结果与真实拉格标签高度一致，验证了数据集和表示方法的有效性。
- 适合音乐信息检索、智能推荐与跨文化音乐分析研究者使用。
拉格分类是印度古典音乐中一项基础的音乐信息检索任务，广泛应用于推荐、教育、档案管理和智能搜索。然而，由于现有方法多依赖标注音频或标签数据，拉格聚类仍缺乏深入探索。虽然标注的旋律片段能捕捉典型模式，但完整的音符序列更能保留时间结构和上下文依赖，更适合数据驱动建模。本文提出RaagBase，一个基于乐谱的文本数据集，包含巴特克汉德作曲作品的音符序列。我们进一步提出一种基于图的拉格结构表示方法，通过建模音符的主导性与缺失性来刻画旋律特征。每部作品作为图中一个节点，节点间的边反映其基于音符频率分布的相似性。应用经典图聚类算法，成功识别出语义一致的拉格组合。实验表明聚类结果与真实拉格标签高度吻合，验证了数据集与表示方法的有效性。数据集已公开：https://anonymous.4open.science/r/RaagBase-5427。
原文摘要 · Abstract (English)
Raag classification is a fundamental MIR task for Hindustani Music, with applications in recommendation, education, archiving, and intelligent search. However, raag clustering remains underexplored, as most existing approaches rely on annotated audio or labeled datasets. While annotated melodic phrases capture characteristic patterns, complete note sequences preserve temporal structure and contextual dependencies, making them more suitable for data-driven modeling. In this work, we introduce RaagBase, a notation-based text dataset consisting of note sequences from compositions by Pt. Bhatkhande. Furthermore we propose a novel graph-based representation of raag structures by modeling the dominance and absence of notes in compositions. Each composition is represented as a node, and the edges between two compositions corresponds the similarities between them based on the note frequency distribution. Further, we apply established graph clustering techniques to identify groups of similar raag compositions. Experimental results demonstrate highly coherent clusters with strong agreement to ground-truth raag labels, thereby validating both the dataset and the proposed representation. The dataset is publicly available at https://anonymous.4open.science/r/RaagBase-5427.
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