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Raga identification from Hindustani classical music signal using compositional properties

机译:使用成分属性从印度斯坦古典音乐信号中识别Raga

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Classification of music signal is a fundamental step for organized archival of music collection and fast retrieval thereafter. For Indian classical music, raga is the basic melodic framework. Manual identification of raga demands high expertise which is not available easily. Thus an automated system for raga identification is of great importance. In this work, we have studied the basic properties of the ragas in North Indian (Hindusthani) classical music and designed the features to capture the same. Pitch based Swara (note) profile is formed. Occurrence and energy distribution of notes generated from the profile are used as features. Note sequence plays an important role in the raga composition. Proposed note co-occurrence matrix summarizes this aspect. An audio clip is represented by these features which have strong correlation with the properties of raga. Support vector machine is used for classification. Experiment is done with a diversified dataset. Performance of the proposed work is compared with two other systems. It is observed that proposed methodology performs better.
机译:音乐信号的分类是组织音乐收集存档并随后快速检索的基本步骤。对于印度古典音乐而言,raga是基本的旋律框架。手动识别raga需要很高的专业知识,而这是不容易获得的。因此,用于Raga识别的自动化系统非常重要。在这项工作中,我们研究了北印度(Hindusthani)古典音乐中的ragas的基本属性,并设计了捕获这些特征的功能。形成基于音高的Swara(音符)轮廓。从配置文件生成的音符的出现和能量分布用作特征。音符序列在raga组成中起重要作用。建议的笔记共现矩阵总结了这一方面。这些功能代表了音频剪辑,这些功能与raga的属性有很强的关联性。支持向量机用于分类。实验是通过多样化的数据集完成的。拟议工作的绩效与其他两个系统进行了比较。据观察,所提出的方法表现更好。

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