系统仿真学报 ›› 2016, Vol. 28 ›› Issue (7): 1622-1627.

• 仿真应用工程 • 上一篇    下一篇

基于特征值合并的语音增强算法

陆慧娟1, 刘亚卿1, 刘砚秋1, 关伟2   

  1. 1.中国计量学院信息工程学院,浙江 杭州310018;
    2.中国计量学院现代科技学院,浙江 杭州 310018
  • 收稿日期:2014-08-12 修回日期:2015-02-01 出版日期:2016-07-08 发布日期:2020-06-04
  • 作者简介:陆慧娟(1962-),女,浙江,博士,教授,研究方向为物联网、模式识别与云计算。
  • 基金资助:
    国家自然科学基金(61272315,60842009),浙江省自然科学基金(Y1110342)

Eigen-value Combination Approach for Speech Enhancement

Lu Huijuan1, Liu Yaqing1, Liu Yanqiu1, Guan Wei2   

  1. 1. College of Information Engineering, China Jiliang University, Hangzhou 310018, China;
    2. College of Moder Science and Technology, China Jiliang University, Hangzhou 310018, China
  • Received:2014-08-12 Revised:2015-02-01 Online:2016-07-08 Published:2020-06-04

摘要: 为进一步抑制噪声,提出一种基于特征值合并的语音增强算法。在经典的内嵌式预白化子空间方法的基础上,用特征值合并来提高语音质量。研究发现,对含噪语音的协方差矩阵进行特征值分解后,大特征值分量主要包含语音信息,而小特征值分量主要包含噪声,特征值分量按特征值从小到大排序后,剔除相邻的小特征值分量,可有效抑制噪声,提高语音质量。相比于其它方法,基于特征值合并的语音增强算法能有效工作于各种噪声环境中,显著提高信噪比,并有更好的语音可懂度。

关键词: 语音增强, 子空间方法, 特征值分解, 语音质量

Abstract: In order to further suppress noise, a kind of speech enhancement algorithm based on Eigen-value merge was proposed. Eigen-values combination was used to improve the speech quality on the basis of the classic embedded pre-whitening subspace methods. The study shows that, after decomposing the covariance matrix of speech signals with noise, the larger Eigen-value component mainly includes speech information, and the smaller Eigen-value component mainly contains noise. Sorted by Eigen-values from small to big, the adjacent large Eigen-value component replaces with small Eigen-value component, which can effectively suppress noise and improve the quality of speech. Compared with other speech enhancement algorithm, this algorithm based on Eigen value merger can work effectively in a variety of noisy environment, significantly improves the SNR, and has better speech intelligibility.

Key words: speech enhancement, subspace method, eigen-value decomposing, speech quality

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