文章摘要
张鑫琪,冯海泓,徐海东.改进的最小均方误差语音增强算法的研究[J].声学技术,2008,(2):230~234
改进的最小均方误差语音增强算法的研究
A study of an improved minimum mean-square error speech enhancement algorithm
投稿时间:2007-09-21  修订日期:2007-12-11
DOI:
中文关键词: 最小均方误差估计  加权噪声估计  谱增益修正  语音增强
英文关键词: MMSE(minimum mean-square error) estimator  weighted noise estimation  spectral gain modification  speech enhancement
基金项目:
作者单位
张鑫琪 中国科学院声学研究所东海研究站, 上海, 200032 
冯海泓 中国科学院声学研究所东海研究站, 上海, 200032 
徐海东 中国科学院声学研究所东海研究站, 上海, 200032 
摘要点击次数: 536
全文下载次数: 2313
中文摘要:
      针对传统最小均方误差谱幅度估计(MMSE-STSA,minimum mean-square error-short time spectral am-plitude)语音增强算法无法有效的跟踪非平稳噪声变化的问题,对一种改进的MMSE-STSA语音增强算法进行了研究和仿真。该算法对背景噪声的估计利用加权噪声估计方法:采用一个非线性函数根据带噪语音信噪比(SNR,signal-to-noise ratio)的变化计算得到相应的加权因子并作用于带噪语音信号,对加权的带噪语音求平均得到估计的背景噪声。算法中的谱增益修正,还可以抑制低信噪比时的残留噪声以及避免对带噪语音的过抵消。实验结果表明,该方法能很好的跟踪非平稳噪声的变化,不仅在增强性能上有很好的效果,同时降低了语音的失真。
英文摘要:
      To achieve good tracking capability without overestimation of various non-stationary noise sources,an improved MMSE-STSA speech enhancement algorithm is proposed in this paper.The algorithm employs weighted noise estimation:the noisy speech is weighted by a weighting factor,which is calculated in accordance with the estimated SNR through a nonlinear function,and the estimated noise is obtained as an average of the weighted noisy speech.The spectrum gain modification in this algorithm can further suppress the residual noise for low SNRs and avoid the excessive suppression.The results indicate that,under the non-stationary noisy environment,the proposed algorithm can not only get a good perfor-mance in enhancement,but also reduce the speech distortion.
查看全文   查看/发表评论  下载PDF阅读器
关闭
function PdfOpen(url){ var win="toolbar=no,location=no,directories=no,status=yes,menubar=yes,scrollbars=yes,resizable=yes"; window.open(url,"",win); } function openWin(url,w,h){ var win="toolbar=no,location=no,directories=no,status=no,menubar=no,scrollbars=yes,resizable=no,width=" + w + ",height=" + h; controlWindow=window.open(url,"",win); } &et=2E8E91ED88C2FEB7A8B111C518AC2F79226A4839741EFD13B4303382EDE0451352758446962A16E05F7A445B286FF463288F10D685DE245969F634509029020E94FD68F01BDD8623891E0B68F4942D78&pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=84529CA2B2E519AC&jid=DDCFCD5ACE1B1E5A6D46213553C850CA&yid=67289AFF6305E306&aid=&vid=&iid=0B39A22176CE99FB&sid=D6354F61445E9456&eid=D9AE183D3F5C3C75&fileno=20080220&flag=1&is_more=0">