• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>WSEAS Transactions on Signal Processing >An Improved Thresholding Method for Wavelet Denoising of Acoustic Signal
【24h】

An Improved Thresholding Method for Wavelet Denoising of Acoustic Signal

机译：一种改进的声学信号小波去噪的阈值方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

To minimize the information loss of acoustic signal and get high SNR in the denoising based on discrete wavelet transform, it is important that the thresholding is suitable for the characteristics of acoustic signal. In this paper, we propose an improved thresholding method to be suitable for the characteristics of acoustic signal. In order to minimize the information loss of acoustic signal in White Gaussian noise, we propose new threshold function to improve the modulus square threshold function. We analyze theoretically a continuity and monotonicity of new threshold function and evaluate the performance of wavelet denoising method based on new thresholding, comparing with Hard, Soft and Modulus square thresholding. Also, we perform the simulation experiment using the various acoustic signals such as mixed acoustic signal of the transient signals, speech signal, shot signal, bird's song signal and sound signal of gun. The results of theoretical analysis for an improved thresholding show that new threshold function solves the problems of constant error and discontinuity, and minimizes the information loss of acoustic signal. The results of simulation experiment show that SNR of an improved thresholding is highest but RMSE and Entropy are smallest. The theoretical analysis and simulation experiments show that an improved thresholding is more appropriate for acoustic signal denoising based on discrete wavelet transform than previous methods.
机译：为了最小化声学信号的信息丢失并基于离散小波变换的去噪能够获得高SNR，重要的是阈值适用于声学信号的特性。在本文中，我们提出了一种改进的阈值化方法，适用于声学信号的特性。为了最小化白色高斯噪声中声学信号的信息丢失，我们提出了新的阈值函数来提高模量方形阈值函数。我们在理论上分析了新阈值函数的连续性和单调性，并评估了基于新阈值的基于新阈值的小波去噪方法的性能，与硬，软和模数阈值相比。此外，我们使用各种声学信号进行仿真实验，例如瞬态信号的混合声信号，语音信号，射击信号，鸟歌信号和枪的声音信号。改进的阈值化的理论分析结果表明，新的阈值函数解决了恒定误差和不连续性的问题，并最大限度地减少了声学信号的信息丢失。仿真实验结果表明，改进的阈值化的SNR是最高的，但RMSE和熵是最小的。理论分析和仿真实验表明，改进的阈值处理比以前的方法基于离散小波变换更适合声学信号去噪。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号