自适应噪声控制耳机中的二次路径模型

Markus Guldenschuh
{"title":"自适应噪声控制耳机中的二次路径模型","authors":"Markus Guldenschuh","doi":"10.1109/ICOSC.2013.6750928","DOIUrl":null,"url":null,"abstract":"The Filtered-x-Least-Mean-Square (FxLMS) is an efficient algorithm for active-noise-control-headphones. It relies on a correct model Ŝ of the secondary-path S which, in the case of headphones, is above all determined by the acoustic path from the loudspeaker to the error-microphone. If the headphones are abruptly lifted or put on, the phase of S changes more than 90° and the formerly correct model Ŝ will suddenly be wrong and the FxLMS might diverge. This paper presents three methods how the divergence of the FxLMS can be avoided. All three methods rely on laboratory measurements under different conditions from tight headphones to completely lifted headphones. First, it is shown how a stable secondary-path model can be derived from the phase information of the measurements. For the second and third method, two secondary-path models are implemented. One for the tight use case and one for the lifted headphones. The current state of the secondary-path is then detected either via an online noise-cancelling-analysis or via an infrasonic test-signal. Comparison with existing approaches shows the robust stability and efficiency of the proposed methods.","PeriodicalId":199135,"journal":{"name":"3rd International Conference on Systems and Control","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Secondary-path models in adaptive-noise-control headphones\",\"authors\":\"Markus Guldenschuh\",\"doi\":\"10.1109/ICOSC.2013.6750928\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Filtered-x-Least-Mean-Square (FxLMS) is an efficient algorithm for active-noise-control-headphones. It relies on a correct model Ŝ of the secondary-path S which, in the case of headphones, is above all determined by the acoustic path from the loudspeaker to the error-microphone. If the headphones are abruptly lifted or put on, the phase of S changes more than 90° and the formerly correct model Ŝ will suddenly be wrong and the FxLMS might diverge. This paper presents three methods how the divergence of the FxLMS can be avoided. All three methods rely on laboratory measurements under different conditions from tight headphones to completely lifted headphones. First, it is shown how a stable secondary-path model can be derived from the phase information of the measurements. For the second and third method, two secondary-path models are implemented. One for the tight use case and one for the lifted headphones. The current state of the secondary-path is then detected either via an online noise-cancelling-analysis or via an infrasonic test-signal. Comparison with existing approaches shows the robust stability and efficiency of the proposed methods.\",\"PeriodicalId\":199135,\"journal\":{\"name\":\"3rd International Conference on Systems and Control\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"3rd International Conference on Systems and Control\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICOSC.2013.6750928\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"3rd International Conference on Systems and Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICOSC.2013.6750928","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

摘要

滤波-x最小均方(FxLMS)算法是一种有效的有源噪声控制耳机算法。它依赖于次级路径S的正确模型Ŝ,在耳机的情况下,次级路径S首先由扬声器到错误麦克风的声学路径决定。如果突然抬起或戴上耳机,S相位变化超过90°,以前正确的模型Ŝ会突然错误,FxLMS可能会偏离。本文提出了避免FxLMS发散的三种方法。这三种方法都依赖于不同条件下的实验室测量,从紧绷的耳机到完全抬起的耳机。首先,展示了如何从测量的相位信息推导出稳定的二次路径模型。对于第二种和第三种方法,实现了两个辅助路径模型。一个用于紧的用例,一个用于抬起的耳机。然后通过在线消噪分析或次声测试信号检测二级路径的当前状态。通过与已有方法的比较,证明了所提方法的鲁棒性、稳定性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Secondary-path models in adaptive-noise-control headphones
The Filtered-x-Least-Mean-Square (FxLMS) is an efficient algorithm for active-noise-control-headphones. It relies on a correct model Ŝ of the secondary-path S which, in the case of headphones, is above all determined by the acoustic path from the loudspeaker to the error-microphone. If the headphones are abruptly lifted or put on, the phase of S changes more than 90° and the formerly correct model Ŝ will suddenly be wrong and the FxLMS might diverge. This paper presents three methods how the divergence of the FxLMS can be avoided. All three methods rely on laboratory measurements under different conditions from tight headphones to completely lifted headphones. First, it is shown how a stable secondary-path model can be derived from the phase information of the measurements. For the second and third method, two secondary-path models are implemented. One for the tight use case and one for the lifted headphones. The current state of the secondary-path is then detected either via an online noise-cancelling-analysis or via an infrasonic test-signal. Comparison with existing approaches shows the robust stability and efficiency of the proposed methods.
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