一种基于多相似性度量融合的蜂鸣声系统查询方法

Lei Wang, Shen Huang, Sheng Hu, Jiaen Liang, Bo Xu
{"title":"一种基于多相似性度量融合的蜂鸣声系统查询方法","authors":"Lei Wang, Shen Huang, Sheng Hu, Jiaen Liang, Bo Xu","doi":"10.1109/ICALIP.2008.4590167","DOIUrl":null,"url":null,"abstract":"Since it is the most natural way for people to search a specific melody in large music database, query by humming/singing is attracting more and more researcherspsila attention in the field of content-based music information retrieval. In this task, note-based and frame-based similarity measures are two commonly used methods. However, in previous works, researchers always focus on one of the two methods alone. In this paper, we propose a novel scheme taking advantage of two different similarity measurements to improve not only the retrieval accuracy but also the retrieving speed. First, Earth Moverpsilas Distance (EMD), which is note-based and much faster, is adopted to eliminate most unlikely candidate. Then, Dynamic Time Warping (DTW), which is frame-based and more accurate, is executed on these surviving candidates. Finally, fusion strategies of these two similarity measurements are employed to improve the performance of whole system. Experiments show our approach can achieve 92.9% accuracy on the database used in MIREX 2006 QBH contest, which is better than those systems participated in that task.","PeriodicalId":175885,"journal":{"name":"2008 International Conference on Audio, Language and Image Processing","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"39","resultStr":"{\"title\":\"An effective and efficient method for query by humming system based on multi-similarity measurement fusion\",\"authors\":\"Lei Wang, Shen Huang, Sheng Hu, Jiaen Liang, Bo Xu\",\"doi\":\"10.1109/ICALIP.2008.4590167\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Since it is the most natural way for people to search a specific melody in large music database, query by humming/singing is attracting more and more researcherspsila attention in the field of content-based music information retrieval. In this task, note-based and frame-based similarity measures are two commonly used methods. However, in previous works, researchers always focus on one of the two methods alone. In this paper, we propose a novel scheme taking advantage of two different similarity measurements to improve not only the retrieval accuracy but also the retrieving speed. First, Earth Moverpsilas Distance (EMD), which is note-based and much faster, is adopted to eliminate most unlikely candidate. Then, Dynamic Time Warping (DTW), which is frame-based and more accurate, is executed on these surviving candidates. Finally, fusion strategies of these two similarity measurements are employed to improve the performance of whole system. Experiments show our approach can achieve 92.9% accuracy on the database used in MIREX 2006 QBH contest, which is better than those systems participated in that task.\",\"PeriodicalId\":175885,\"journal\":{\"name\":\"2008 International Conference on Audio, Language and Image Processing\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-07-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"39\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 International Conference on Audio, Language and Image Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICALIP.2008.4590167\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 International Conference on Audio, Language and Image Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICALIP.2008.4590167","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 39

摘要

在基于内容的音乐信息检索领域,哼唱查询是人们在大型音乐数据库中搜索特定旋律最自然的方式,因此越来越受到研究者的关注。在本任务中,基于笔记和基于框架的相似性度量是两种常用的方法。然而,在以往的研究中,研究人员总是只关注这两种方法中的一种。在本文中,我们提出了一种利用两种不同的相似度量来提高检索精度和检索速度的新方案。首先,采用基于笔记且速度更快的地球移动距离(EMD)来排除最不可能的候选对象。然后,对这些幸存的候选对象执行基于帧的更精确的动态时间翘曲(DTW)。最后,采用这两种相似性度量的融合策略来提高整个系统的性能。实验表明,该方法在MIREX 2006 QBH竞赛数据库上的准确率达到了92.9%,优于参加该竞赛的系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An effective and efficient method for query by humming system based on multi-similarity measurement fusion
Since it is the most natural way for people to search a specific melody in large music database, query by humming/singing is attracting more and more researcherspsila attention in the field of content-based music information retrieval. In this task, note-based and frame-based similarity measures are two commonly used methods. However, in previous works, researchers always focus on one of the two methods alone. In this paper, we propose a novel scheme taking advantage of two different similarity measurements to improve not only the retrieval accuracy but also the retrieving speed. First, Earth Moverpsilas Distance (EMD), which is note-based and much faster, is adopted to eliminate most unlikely candidate. Then, Dynamic Time Warping (DTW), which is frame-based and more accurate, is executed on these surviving candidates. Finally, fusion strategies of these two similarity measurements are employed to improve the performance of whole system. Experiments show our approach can achieve 92.9% accuracy on the database used in MIREX 2006 QBH contest, which is better than those systems participated in that task.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信