Automated Transcription for Raga Recognition and Classification in Indian Classical Music Using Machine Learning

B. Gowrishankar, U. B. Nagappa
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引用次数: 1

Abstract

Raga recognition is only possible by trained musician to understand the notes based on the lead voice but a beginner is unable to decode the notes. This is significant for current scenarios in developing an automated note transcription system in Indian Classical Music (ICM). In the present research, various properties of raga and the machine learning techniques that are used for identifying the raga by a machine rather than a human or music expert are surveyed. The previously developed automatic raga recognition techniques using Carnatic and Hindustani Music, the main drawbacks and the improvements required are discussed. The present research work discusses about the future proposed models for automatic raga recognition using pitch detection algorithm, finding Tuning Offset, and Note Segmentation process. The proposed model will obtain better accuracy more than 96 % when compared to the existing CNN, GMM that obtained accuracy of 94 % and 95 %.
使用机器学习的印度古典音乐中拉格识别和分类的自动转录
只有训练有素的音乐家才能根据主音理解拉格的音符,但初学者无法解码这些音符。这对于当前印度古典音乐(ICM)中自动音符转录系统的开发具有重要意义。在目前的研究中,拉格的各种特性和机器学习技术被用于识别拉格由机器而不是人类或音乐专家进行了调查。讨论了以前开发的使用卡纳蒂克和印度斯坦音乐的拉格自动识别技术,主要缺点和需要改进的地方。目前的研究工作讨论了未来提出的拉格自动识别模型,包括音调检测算法、寻找调谐偏移和音符分割过程。与现有的CNN、GMM的准确率分别为94%和95%相比,该模型的准确率达到了96%以上。
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