Distributed combined acoustic echo cancellation and noise reduction using GEVD-based distributed adaptive node specific signal estimation with prior knowledge

Santiago Ruiz, T. Waterschoot, M. Moonen
{"title":"Distributed combined acoustic echo cancellation and noise reduction using GEVD-based distributed adaptive node specific signal estimation with prior knowledge","authors":"Santiago Ruiz, T. Waterschoot, M. Moonen","doi":"10.23919/Eusipco47968.2020.9287337","DOIUrl":null,"url":null,"abstract":"Distributed combined acoustic echo cancellation (AEC) and noise reduction (NR) in a wireless acoustic sensor network (WASN) is tackled by using a specific version of the PK-GEVD-DANSE algorithm (cfr. [1]). Although this algorithm was initially developed for distributed NR with partial prior knowledge of the desired speech steering vector, it is shown that it can also be used for AEC combined with NR. Simulations have been carried out using centralized and distributed batch-mode implementations to verify the performance of the algorithm in terms of AEC quantified with the echo return loss enhancement (ERLE), as well as in terms of the NR quantified with the signal- to-noise ratio (SNR).","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"363 1","pages":"206-210"},"PeriodicalIF":0.0000,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 28th European Signal Processing Conference (EUSIPCO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/Eusipco47968.2020.9287337","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

Distributed combined acoustic echo cancellation (AEC) and noise reduction (NR) in a wireless acoustic sensor network (WASN) is tackled by using a specific version of the PK-GEVD-DANSE algorithm (cfr. [1]). Although this algorithm was initially developed for distributed NR with partial prior knowledge of the desired speech steering vector, it is shown that it can also be used for AEC combined with NR. Simulations have been carried out using centralized and distributed batch-mode implementations to verify the performance of the algorithm in terms of AEC quantified with the echo return loss enhancement (ERLE), as well as in terms of the NR quantified with the signal- to-noise ratio (SNR).
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
基于gevd的分布式自适应节点特定信号估计与先验知识的分布式组合声回波抵消与降噪
使用特定版本的PK-GEVD-DANSE算法(cfr)解决了无线声传感器网络(nn)中的分布式联合声回波抵消(AEC)和降噪(NR)问题。[1])。虽然该算法最初是针对具有部分先验知识的期望语音转向向量的分布式NR开发的,但研究表明,它也可以用于与NR结合的AEC。使用集中式和分布式批处理模式进行了仿真,以验证该算法在用回波回波损耗增强(ERLE)量化的AEC方面的性能,以及用信噪比(SNR)量化的NR方面的性能。
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
Eusipco 2021 Cover Page A graph-theoretic sensor-selection scheme for covariance-based Motor Imagery (MI) decoding Hidden Markov Model Based Data-driven Calibration of Non-dispersive Infrared Gas Sensor Deep Transform Learning for Multi-Sensor Fusion Two Stages Parallel LMS Structure: A Pipelined Hardware Architecture
×
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