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An algorithm for Morlet wavelet transform based on generalized discrete Fourier transform

机译:一种基于广义离散傅立叶变换的Morlet小波变换算法

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Continuous wavelet transform (CWT) is a linear convolution of signal and wavelet function for a fixed scale. This paper studies the algorithm of CWT with Morlet wavelet as mother wavelet by using nonzero-padded linear convolution. The time domain filter, which is a non-causal filter, is the sample of wavelet function. By making generalized discrete Fourier transform (GDFT) and inverse transform for this filter, we can get a geometrically weighted periodic extension of the filter when evaluated outside its original support. From this extension of the time domain filter, we can get a causal filter. In this paper, GDFT-based algorithm for CWT, which has a more concise form than that of linear convolution proposed by Jorge Martinez, is constructed by using this causal filter. The analytic expression of the GDFT of this filter, which is essential for GDFT-based algorithm for CWT, is deduced in this paper. The numerical experiments show that the calculation results of GDFT-based algorithm are stable and reliable; the running speed of GDFT-based algorithm is faster than that of the other two algorithms studied in our previous work.
机译:连续小波变换(CWT)是用于固定刻度的信号和小波函数的线性卷积。本文采用非零填充线性卷积研究了Morlet小波与Morlet小波的CWT算法。作为非因果滤波器的时域过滤器是小波函数的样本。通过对该过滤器进行广义离散的傅立叶变换(GDFT)和逆变换,我们可以在其原始支持之外进行评估时,可以获得滤波器的几何加权周期性扩展。从时域过滤器的此扩展,我们可以获得因果筛选器。本文通过使用该因果滤波器构建了CWT的基于GDFT的CWT算法,其具有比Jorge Martinez提出的线性卷积更简洁的形式。本文推出了该滤波器GDFT的解析表达,这对于基于GDFT的CWT算法是必不可少的。数值实验表明,基于GDFT的算法的计算结果稳定可靠;基于GDFT的算法的运行速度比我们以前的工作中研究的其他两种算法的运行速度快。

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