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Clustering of Instruments in Carnatic Music for Content Based Information Retrieval

机译：基于内容的信息检索的Carnatic音乐中的仪器聚类

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摘要

Music Information Retrieval (MIR) focuses on retrieving useful information from collection of music. The objective of research work in this paper is to explore clustering approaches which can be useful in automatically mining the content from Carnatic instrumental music. The content to be retrieved is the instrument that is primarily used to play the song. Carnatic music songs with ten different instruments namely, Flute, Harmonium, Mandolin, Nagaswara, Santoor, Saxophone, Sitar, Shehnai, Veena and Violin are considered as input. Mel Frequency Cepstral Coefficients (MFCC) and Linear Predictive Coefficients (LPC) features are used for representing music information. In the first step, visualization technique is used to explore the capability of different features in distinguishing Carnatic music with different instruments. Then different clustering techniques are used for understanding natural way of grouping among this instrumental music. A discussion on the comparison of instrument clustering results with different algorithms, combined with various features is also presented.
机译：音乐信息检索（MIR）侧重于从音乐集合中检索有用的信息。本文研究工作的目的是探索聚类方法，这些方法可用于自动挖掘狂欢乐器音乐的内容。要检索的内容是主要用于播放歌曲的仪器。有十种不同乐器的Carnatic音乐歌曲即长笛，谐波，曼陀林，长浪，圣托，萨克斯管，Sitar，Shehnai，Veena和小提琴被视为投入。 MEL频率谱系数（MFCC）和线性预测系数（LPC）特征用于表示音乐信息。在第一步中，可视化技术用于探索与不同仪器区分Carnatic音乐的不同特征的能力。然后，不同的聚类技术用于了解这种乐器音乐之间分组的自然方式。还介绍了对不同算法的仪器聚类结果比较的讨论，结合各种特征。

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