音频信号识别的可靠性评估

E. Gershikov, Shiran Gabler
{"title":"音频信号识别的可靠性评估","authors":"E. Gershikov, Shiran Gabler","doi":"10.1109/INTERCON.2018.8526407","DOIUrl":null,"url":null,"abstract":"In this paper we propose reliability measures for algorithms that recognize an audio signal within a database of music recordings when only a short segment of it is available. Using these measures, we test the reliability of two algorithms: one based on spectrogram peaks and one based on MFCC features. We compare the performance of the two methods and conclude about the usability and usefulness of our evaluation.","PeriodicalId":305576,"journal":{"name":"2018 IEEE XXV International Conference on Electronics, Electrical Engineering and Computing (INTERCON)","volume":"78 26","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Reliability Evaluation of Audio Signal Recognition\",\"authors\":\"E. Gershikov, Shiran Gabler\",\"doi\":\"10.1109/INTERCON.2018.8526407\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we propose reliability measures for algorithms that recognize an audio signal within a database of music recordings when only a short segment of it is available. Using these measures, we test the reliability of two algorithms: one based on spectrogram peaks and one based on MFCC features. We compare the performance of the two methods and conclude about the usability and usefulness of our evaluation.\",\"PeriodicalId\":305576,\"journal\":{\"name\":\"2018 IEEE XXV International Conference on Electronics, Electrical Engineering and Computing (INTERCON)\",\"volume\":\"78 26\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE XXV International Conference on Electronics, Electrical Engineering and Computing (INTERCON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INTERCON.2018.8526407\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE XXV International Conference on Electronics, Electrical Engineering and Computing (INTERCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INTERCON.2018.8526407","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

在本文中,我们提出了在只有一小段可用的音乐录音数据库中识别音频信号的算法的可靠性措施。利用这些度量,我们测试了两种算法的可靠性:一种基于谱图峰值,另一种基于MFCC特征。我们比较了两种方法的性能,并总结了我们评估的可用性和有用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reliability Evaluation of Audio Signal Recognition
In this paper we propose reliability measures for algorithms that recognize an audio signal within a database of music recordings when only a short segment of it is available. Using these measures, we test the reliability of two algorithms: one based on spectrogram peaks and one based on MFCC features. We compare the performance of the two methods and conclude about the usability and usefulness of our evaluation.
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:481959085
Book学术官方微信