上海交通大学学报

上海交通大学学报（自然版） ›› 2012, Vol. 46 ›› Issue (09): 1445-1449.

• 无线电电子学、电信技术 • 上一篇    下一篇

基于尺度噪声能量估计的自适应语音去噪算法

 谢巍盛, 杨根科   

  1. (上海交通大学 自动化系, 系统控制与信息处理教育部重点实验室, 上海 200240)
  • 收稿日期:2011-09-14 出版日期:2012-09-28 发布日期:2012-09-28
  • 基金资助:

    国家自然科学基金资助项目(61074150)，国家高技术研究发展规划（973）项目(2010CB731803)

An Adaptive Speech Signal De-noising Algorithm Based on Estimation of Scaled Noise Energy

 XIE  Wei-Sheng, YANG  Gen-Ke   

  1. (Department of Automation, Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai Jiaotong University, Shanghai 200240, China)
  • Received:2011-09-14 Online:2012-09-28 Published:2012-09-28

摘要：   摘要： 
针对语音增强技术中的信号去噪问题,提出了一种非线性小波自适应阈值去噪方法.该方法采用一个改进的阈值函数,克服了传统软、硬阈值函数的缺陷；在阈值选取规则中，引入尺度相关去噪法而自适应地选取尺度阈值,利用小波系数在空间尺度的相关性进行尺度噪声能量的估计,根据所得尺度噪声能量来选取对应尺度层中的最佳小波系数并作为该尺度的阈值；同时，应用该方法对不同强度噪声背景下的语音信号进行去噪.结果表明，其具有较好的降噪性能. 关键词： 
语音信号； 滤波; 小波变换； 噪声能量； 自适应阈值 中图分类号：  TN 912.3
文献标志码：  A    

Abstract: A novel scheme of wavelet thresholding for speech signal de-noising was proposed. The scheme introduces a new thresholding function to overcome the drawbacks of traditional soft and hard thresholding functions. This newly introduced function is continuous at the threshold, so that it can curb the pseudogibbs phenomena effectively. Besides, it can reduce constant bias between the original wavelet coefficient and the estimated one, which helps to preserve the feature of the signal. With this threshold function, an adaptively selective algorithm of wavelet threshold was then proposed. This algorithm has both the virtues of spatial selective noise filtration method and the traditional threshold denoising method. It takes advantage of the inter-scale correlations of the wavelet coefficients to estimate the energy of scaled noise signal. In this algorithm, the threshold in each scale is selected based on the scaled noise energy. The simulations demonstrate that the proposed method is superior to some other existing adaptive wavelet de-noising methods.  

Key words: speech signal, de-noising, wavelet transform, noise energy, adaptive thresholding