Bo Xiao, Bingpeng Zhou, Qingchun Chen, Chengyun Zhang
{"title":"相关噪声环境下基于能量的非平稳声源定位","authors":"Bo Xiao, Bingpeng Zhou, Qingchun Chen, Chengyun Zhang","doi":"10.1109/icccs55155.2022.9845869","DOIUrl":null,"url":null,"abstract":"The generalized energy-based acoustic source localization problem in a correlated noise environment has been addressed in this paper. At first, a Gaussian distribution was employed to characterize the placement deviations of the sensors. And a fractional Gaussian noise (fGn) statistical model was utilized to characterize the acoustic source and noise correlation. Based on Bayesian estimation theory, a generalized energy-based acoustic source localization scheme was presented, wherein the particle-assisted stochastic search (PASS) algorithm was used to determine the acoustic source position from the observations by sensor arrays. Experiment results were presented to validate the effectiveness of the energy-based acoustic source localization, even in the complicated environment with correlated noise, sensor placement deviations, and non-stationarity characteristics.","PeriodicalId":121713,"journal":{"name":"2022 7th International Conference on Computer and Communication Systems (ICCCS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Energy-based Nonstationary Acoustic Source Localization in Correlated Noise Environment\",\"authors\":\"Bo Xiao, Bingpeng Zhou, Qingchun Chen, Chengyun Zhang\",\"doi\":\"10.1109/icccs55155.2022.9845869\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The generalized energy-based acoustic source localization problem in a correlated noise environment has been addressed in this paper. At first, a Gaussian distribution was employed to characterize the placement deviations of the sensors. And a fractional Gaussian noise (fGn) statistical model was utilized to characterize the acoustic source and noise correlation. Based on Bayesian estimation theory, a generalized energy-based acoustic source localization scheme was presented, wherein the particle-assisted stochastic search (PASS) algorithm was used to determine the acoustic source position from the observations by sensor arrays. Experiment results were presented to validate the effectiveness of the energy-based acoustic source localization, even in the complicated environment with correlated noise, sensor placement deviations, and non-stationarity characteristics.\",\"PeriodicalId\":121713,\"journal\":{\"name\":\"2022 7th International Conference on Computer and Communication Systems (ICCCS)\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-04-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 7th International Conference on Computer and Communication Systems (ICCCS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/icccs55155.2022.9845869\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 7th International Conference on Computer and Communication Systems (ICCCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/icccs55155.2022.9845869","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Energy-based Nonstationary Acoustic Source Localization in Correlated Noise Environment
The generalized energy-based acoustic source localization problem in a correlated noise environment has been addressed in this paper. At first, a Gaussian distribution was employed to characterize the placement deviations of the sensors. And a fractional Gaussian noise (fGn) statistical model was utilized to characterize the acoustic source and noise correlation. Based on Bayesian estimation theory, a generalized energy-based acoustic source localization scheme was presented, wherein the particle-assisted stochastic search (PASS) algorithm was used to determine the acoustic source position from the observations by sensor arrays. Experiment results were presented to validate the effectiveness of the energy-based acoustic source localization, even in the complicated environment with correlated noise, sensor placement deviations, and non-stationarity characteristics.