经验模态分解与正则化阈值化用于语音去噪
Empirical mode decomposition and normalshrink tresholding for speech denoising
AI总结：
本文提出基于经验模态分解的语音去噪方法，通过自适应分解信号为固有振荡分量，利用阈值化技术提升信噪比并减少信号退化。
摘要：
本文提出一种基于经验模态分解（EMD）的信号去噪方案。该去噪方法是一种完全数据驱动的方法。通过称为sifting过程的分解算法，将噪声信号自适应地分解为固有模态函数（IMFs）。该方法的基本原理是将语音信号分解为若干段，每帧被分类为信号主导或噪声主导，然后利用已过滤或阈值化的IMF信号主导帧重新构建信号。通过大量仿真表明，EMD提高了信噪比并解决了信号退化问题。该去噪方法应用于不同噪声水平的实信号，并与Winner和通用阈值的DONOHO和JOHNSTONE[11]的软阈值和硬阈值进行比较。分析了噪声水平对所提去噪性能的影响。研究仅限于受加性白高斯随机噪声污染的信号。
英文摘要：
In this paper a signal denoising scheme based on Empirical mode decomposition (EMD) is presented. The denoising method is a fully data driven approach. Noisy signal is decomposed adaptively into intrinsic oscillatory components called Intrinsic mode functions (IMFs) using a decomposition algorithm called sifting process. The basic principle of the method is to decompose a speech signal into segments each frame is categorised as either signal-dominant or noise-dominant then reconstruct the signal with IMFs signal dominant frame previously filtered or thresholded. It is shown, on the basis of intensive simulations that EMD improves the signal to noise ratio and address the problem of signal degradation. The denoising method is applied to real signal with different noise levels and the results compared to Winner and universal threshold of DONOHO and JOHNSTONE [11] with soft and hard tresholding. The effect of level noise value on the performances of the proposed denoising is analysed. The study is limited to signals corrupted by additive white Gaussian random noise.
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