RISC:一种用于多分量信号分解的改进Costas估计-预测滤波器组

R. Kumaresan, C. S. Ramalingam, A. Rao
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引用次数: 15

摘要

我们提出了一种改进版本的估计-预测器滤波器组,最初由Costas[1]提出,用于分解和跟踪信号中存在的多个非平稳正弦分量。每个分量被分配一个信号估计器,它是一个因果滤波器,和一个预测器。估计器-预测器组合估计其信号分量的下一个时间样本,然后从复合输入信号中减去。理想情况下,没有信号分量会干扰对其他分量的准确估计。然而,科斯塔斯的预测器在有快速变化的信封的组件时表现不佳。在本文中,我们提出了一种改进的预测器,通过最小化预测误差准则来补偿因果滤波在信号分量中引入的群延迟。使用这种改进的预测器,使用计算机合成的多分量信号,我们表明,与Costas的方法相比,我们实现了更清晰的信号分量分离。我们还证明了该方法可以用于分离浊音中的谐波偏分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
RISC: An Improved Costas Estimator-Predictor Filter Bank For Decomposing Multicomponent Signals
We propose an improved version of an estimator-predictor filter bank, originally proposed by Costas [l], for decomposing and tracking multiple, nonstationary sinusoidal components present in a signal. Each component is assigned a signal estimator which is a causal filter, and a predictor. The estimator-predictor combination estimates the next time-sample of its signal component, which is then subtracted from the composite input signal. Ideally, no signal component will then interfere with accurate estimation of the others. However, Costas’s predictor performs poorly when there are components with rapidly changing envelopes. In this paper, we propose an improved predictor that compensates for the group delay introduced in the signal components by the causal filtering, by minimizing a prediction error criterion. With this improved predictor, using a computer synthesized multicomponent signal, we show that we achieve cleaner separation of signal components when compared with Costas’s method. We also show that this method can be used to separate the essentially harmonic partials in voiced speech.
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