Article(id=1309639520379823090, tenantId=1146029695717560320, journalId=1189645257101713411, issueId=1309639513614414014, articleNumber=null, orderNo=null, doi=10.19822/j.cnki.1671-6329.20250071, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790171993584, onlineDateStr=2026-09-23, pubDate=1785859200000, pubDateStr=2026-08-05, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790171993584, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790171993584, creator=13701087609, updateTime=1790171993584, updator=13701087609, issue=Issue{id=1309639513614414014, tenantId=1146029695717560320, journalId=1189645257101713411, year='2026', volume='', issue='8', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1785859200000', pubDateStr='2026-08-05', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790171991971, creator='13701087609', updateTime=1790233588708, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309897869121913162, tenantId=1146029695717560320, journalId=1189645257101713411, issueId=1309639513614414014, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309897869121913163, tenantId=1146029695717560320, journalId=1189645257101713411, issueId=1309639513614414014, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=49, endPage=58, ext={EN=ArticleExt(id=1309639521730388979, articleId=1309639520379823090, tenantId=1146029695717560320, journalId=1189645257101713411, language=EN, title=Research on the Active Sound Management System of Automotive Intelligent Cockpit, columnId=1309639514595881152, journalTitle=Automotive Digest, columnName=Special Topic on Security Verification and Evaluation of Intelligent Connected Vehicles, runingTitle=null, highlight=null, articleAbstract=

To reduce the low-frequency noise generated during the driving of electric vehicles and improve riding comfort, an active sound management system for electric vehicle cabins is designed. It is based on the FbLMS algorithm, which classifies the noise in the cockpit into useful noise and useless noise according to the length of occurrence and burstiness. The two types of noise are separated and processed through the audio of the vehicle and the error microphone arranged in the target noise reduction position. The results show that, by the active sound management system based on the mutation factor λ, the retention of useful mutational signals is achieved while the useless noise is suppressed, thus fulfilling the objective of active sound management in the intelligent cockpit.

, authors=Yilin Zhu¹, Huaifeng Cui¹, Jingtao Ma^{1, 2}, Cancan Tan¹, Huai Cui¹, authorsList=Yilin Zhu, Huaifeng Cui, Jingtao Ma, Cancan Tan, Huai Cui, authorCompany=null, correspAuthors=Huaifeng Cui, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1309639521982047220, articleId=1309639520379823090, tenantId=1146029695717560320, journalId=1189645257101713411, language=CN, title=汽车智能座舱的主动声音管理系统研究, columnId=1309639514876899522, journalTitle=汽车文摘, columnName=智能网联汽车安全验证与评估专题, runingTitle=null, highlight=null, articleAbstract=

为了降低电动汽车行驶中的低频噪声，提高驾乘舒适性，设计了一种针对电动汽车智能座舱的主动声音管理系统。以FbLMS算法为基础，将座舱内的噪声以出现时间长短和突发性分为有用噪声和无用噪声，通过车内音响和布置在目标降噪位置的误差传声器，对噪声进行分离和处理。结果表明，通过基于突变因子λ的主动声音管理系统，实现了对有用突变信号的保留，同时抑制了无用噪声，达成了智能座舱声音主动管理的目标。

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