Identification of allied raagas in Carnatic music

Prithvi Upadhyaya, M. SumaS., S. Koolagudi
{"title":"Identification of allied raagas in Carnatic music","authors":"Prithvi Upadhyaya, M. SumaS., S. Koolagudi","doi":"10.1109/IC3.2015.7346666","DOIUrl":null,"url":null,"abstract":"In this work, an effort has been made to differentiate the allied raagas in Carnatic music. Allied raagas are the raagas that are composed using same set of notes. The features derived from the pitch sequence are used for differentiating these raagas. The coefficients of legendre polynomials, used to fit the pitch contours of the song clips are used for identifying raagas. Obtained features are validated using different classifiers such as Neural networks, Naive Bayes, Multi class classifier, Bagging and Random forest. The proposed system is tested on 4 sets of allied raagas. Naive Bayes classifier gives an average accuracy of 86.67% for allied set of Todi-Dhanyasi and Multi class classifier gives an average accuracy of 86.67% for allied set of Kharaharapriya-Anandabhairavi-Reethigoula. In general, Neural network classifier performance is found to be better than other classifiers.","PeriodicalId":217950,"journal":{"name":"2015 Eighth International Conference on Contemporary Computing (IC3)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 Eighth International Conference on Contemporary Computing (IC3)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IC3.2015.7346666","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

In this work, an effort has been made to differentiate the allied raagas in Carnatic music. Allied raagas are the raagas that are composed using same set of notes. The features derived from the pitch sequence are used for differentiating these raagas. The coefficients of legendre polynomials, used to fit the pitch contours of the song clips are used for identifying raagas. Obtained features are validated using different classifiers such as Neural networks, Naive Bayes, Multi class classifier, Bagging and Random forest. The proposed system is tested on 4 sets of allied raagas. Naive Bayes classifier gives an average accuracy of 86.67% for allied set of Todi-Dhanyasi and Multi class classifier gives an average accuracy of 86.67% for allied set of Kharaharapriya-Anandabhairavi-Reethigoula. In general, Neural network classifier performance is found to be better than other classifiers.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
卡纳蒂克音乐中联合拉格舞曲的鉴定
在这部作品中,人们努力区分卡纳蒂克音乐中的联合拉格。联合拉格是使用相同音符组合而成的拉格。从音高序列中得到的特征被用来区分这些拉格。用来拟合歌曲片段的音高轮廓的勒让德多项式系数用于识别拉格。使用神经网络、朴素贝叶斯、多类分类器、Bagging和随机森林等不同的分类器对得到的特征进行验证。所提出的系统在4组联合raagas上进行了测试。朴素贝叶斯分类器对Todi-Dhanyasi的联合集的平均准确率为86.67%,多类分类器对Kharaharapriya-Anandabhairavi-Reethigoula的联合集的平均准确率为86.67%。一般来说,神经网络分类器的性能优于其他分类器。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Implementing security technique on generic database Pruned feature space for metamorphic malware detection using Markov Blanket Mitigation of desynchronization attack during inter-eNodeB handover key management in LTE Task behaviour inputs to a heterogeneous multiprocessor scheduler Hand written digit recognition system for South Indian languages using artificial neural networks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1