声学回波消除的集隶属度偏更新SMFTF算法

M. Ramdane, A. Benallal, Tedjani Ayoub
{"title":"声学回波消除的集隶属度偏更新SMFTF算法","authors":"M. Ramdane, A. Benallal, Tedjani Ayoub","doi":"10.1109/ICAECCS56710.2023.10104623","DOIUrl":null,"url":null,"abstract":"In recent years, computational complexity reduction (CCR) has gained greater importance in the development of dedicated adaptive algorithms in the field of acoustic echo cancellation (AEC). In this paper, a low-cost, adaptive filtering algorithm is proposed on the basis of the Reduced Partial Update Simplified Fast Transversal Filter (RPU- SMFTF) algorithm and Set Membership (SM) principle. The suggested algorithm is termed Set Membership-RPU-SMFT algorithm (SM-RPU-SMFTF). The benefits of the suggested algorithm, as opposed to the Normalized Least Mean Square (NLMS) and RPU-SMFTF adaptive algorithms, are outlined using simulation results. From the results, we infer that the SM-RPUSMFTF algorithm provides a good convergence phase, tracking ability and better steady-state phase than the other comparison algorithms with reduced the computational complexity.","PeriodicalId":447668,"journal":{"name":"2023 International Conference on Advances in Electronics, Control and Communication Systems (ICAECCS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Set-Membership Parial Update SMFTF Algorithm For Acoustic Echo Cancellation\",\"authors\":\"M. Ramdane, A. Benallal, Tedjani Ayoub\",\"doi\":\"10.1109/ICAECCS56710.2023.10104623\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In recent years, computational complexity reduction (CCR) has gained greater importance in the development of dedicated adaptive algorithms in the field of acoustic echo cancellation (AEC). In this paper, a low-cost, adaptive filtering algorithm is proposed on the basis of the Reduced Partial Update Simplified Fast Transversal Filter (RPU- SMFTF) algorithm and Set Membership (SM) principle. The suggested algorithm is termed Set Membership-RPU-SMFT algorithm (SM-RPU-SMFTF). The benefits of the suggested algorithm, as opposed to the Normalized Least Mean Square (NLMS) and RPU-SMFTF adaptive algorithms, are outlined using simulation results. From the results, we infer that the SM-RPUSMFTF algorithm provides a good convergence phase, tracking ability and better steady-state phase than the other comparison algorithms with reduced the computational complexity.\",\"PeriodicalId\":447668,\"journal\":{\"name\":\"2023 International Conference on Advances in Electronics, Control and Communication Systems (ICAECCS)\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-03-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 International Conference on Advances in Electronics, Control and Communication Systems (ICAECCS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICAECCS56710.2023.10104623\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Conference on Advances in Electronics, Control and Communication Systems (ICAECCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAECCS56710.2023.10104623","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

近年来,计算复杂度降低(CCR)在声学回波抵消(AEC)领域的专用自适应算法开发中变得越来越重要。基于RPU- SMFTF算法和集合隶属度(SM)原理,提出了一种低成本的自适应滤波算法。该算法被称为Set Membership-RPU-SMFT算法(SM-RPU-SMFTF)。与归一化最小均方(NLMS)和RPU-SMFTF自适应算法相比,本文使用仿真结果概述了所建议算法的优点。结果表明,SM-RPUSMFTF算法具有较好的收敛相位、跟踪能力和较好的稳态相位,降低了计算复杂度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Set-Membership Parial Update SMFTF Algorithm For Acoustic Echo Cancellation
In recent years, computational complexity reduction (CCR) has gained greater importance in the development of dedicated adaptive algorithms in the field of acoustic echo cancellation (AEC). In this paper, a low-cost, adaptive filtering algorithm is proposed on the basis of the Reduced Partial Update Simplified Fast Transversal Filter (RPU- SMFTF) algorithm and Set Membership (SM) principle. The suggested algorithm is termed Set Membership-RPU-SMFT algorithm (SM-RPU-SMFTF). The benefits of the suggested algorithm, as opposed to the Normalized Least Mean Square (NLMS) and RPU-SMFTF adaptive algorithms, are outlined using simulation results. From the results, we infer that the SM-RPUSMFTF algorithm provides a good convergence phase, tracking ability and better steady-state phase than the other comparison algorithms with reduced the computational complexity.
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