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A new pre-whitening transform domain LMS algorithm and its application to speech denoising

机译：一种新的变白前变换域LMS算法及其在语音去噪中的应用

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摘要

In this paper, we propose a new pre-whitening transform domain LMS algorithm. The main idea is to introduce a pre-whitening using a simple finite impulse response decorrelation filter of order one before applying the transform to reinforce its decorrelation. The resulting algorithm has the advantage of using any transform even with low decorrelation. This advantage can be exploited to consider transforms having lower computational and structural complexities than those of the classical transforms. For this purpose, we also investigate the use of other transforms, namely the parametric Fourier and Hartley transforms. This investigation is accomplished by studying the eigenvalue spreads obtained by a given parametric transform and then finding the value of the parameter corresponding to the minimum eigenvalue spread, which is equivalent to the best mean square error (MSE) convergence behavior. This approach provides new attractive transforms for the proposed algorithm. Moreover, we consider the adaptive speech denoising as an application to evaluate the performance of the proposed algorithm. The comparisons between the proposed and conventional algorithms for different transforms are performed in terms of the computational complexity, MSE convergence speed, reached steady state level, residual noise in the denoised signal, steady state excess MSE, misadjustment and output SNR.
机译：在本文中，我们提出了一种新的预白化变换域LMS算法。主要思想是在应用变换以增强其去相关之前，使用阶数的简单有限脉冲响应去相关滤波器引入预白化。所得算法具有即使在低去相关时也可以使用任何变换的优点。可以利用这一优势来考虑比传统转换具有更低的计算和结构复杂度的转换。为此，我们还研究了其他变换的使用，即参数傅里叶变换和Hartley变换。通过研究通过给定参数变换获得的特征值散度，然后找到与最小特征值散度相对应的参数值来完成此研究，该特征值等效于最佳均方误差（MSE）收敛行为。该方法为提出的算法提供了新的有吸引力的变换。此外，我们认为自适应语音去噪是一种用于评估所提出算法性能的应用。在计算复杂度，MSE收敛速度，达到稳态水平，去噪信号中的残留噪声，稳态多余MSE，失调和输出SNR方面，对建议的算法和常规算法进行了不同变换的比较。

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