发表机构
XIM University（西姆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对北印度音乐拉格聚类未充分探索的问题，提出基于Pt. Bhatkhande作品音符序列的拉格基础数据集，用图形表示拉格结构，通过音符频率分布体现作品相似性，应用聚类技术，实验验证了数据集及表示的有效性。
AI 中文摘要
拉格分类是北印度音乐中一项基本的音乐信息检索任务，在推荐、教育、存档和智能搜索等方面有应用。然而，拉格聚类仍未得到充分探索，因为大多数现有方法依赖于带注释的音频或标记数据集。虽然带注释的旋律短语捕捉了特征模式，但完整的音符序列保留了时间结构和上下文依赖性，使其更适合数据驱动建模。在这项工作中，我们引入了拉格基础数据集，它是一个基于记谱法的文本数据集，由Pt. Bhatkhande作品中的音符序列组成。我们还通过对作品中音符的主导和缺失进行建模，提出了一种新颖的基于图形的拉格结构表示。每个作品都表示为一个节点，两个作品之间的边对应于基于音符频率分布的它们之间的相似性。我们应用既定的图形聚类技术来识别相似拉格作品的组。实验结果表明聚类高度连贯，与真实的拉格标签高度一致，从而验证了数据集和所提出的表示。该数据集可通过此https URL公开获取。
英文摘要
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.
