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