基于移频的语音信号变分模分解方法

Wenyang Liu, Weiping Hu, Deli Fu
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摘要

为了解决语音分解过程中出现的模式混叠和模式混叠问题,本文提出了一种基于变分模分解(VMD)的语音信号分解方法:变分模分解-频移(VMD - fs)。该方法利用VMD对语音信号基频的良好提取能力,设置特定的载波参数将语音信号的频率移至较低的频率,然后对VMD应用特定的参数和迭代方法对语音信号进行分解,从而得到构成语音信号的真实imf。通过对真实语音信号的分解实验,对比经验模态分解(Empirical mode decomposition, EMD)和原有的VMD方法,证明VMD- fs解决了语音信号分解过程中出现的模式混叠和模式混叠问题。从以上三种方法分解结果的均方误差(MSE)可以证明VMD- fs优于EMD和VMD方法
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Frequency Shifting-based Variational Mode Decomposition Method for Speech Signal Decomposition
In order to solve the problem of mode mixing and mode aliasing arising from speech decomposition, this paper proposes a speech signal decomposition method based on Variational Mode Decomposition (VMD): Variational Mode Decomposition-Frequency Shifting, VMD-FS). The method takes advantage of the VMD's good extraction of the fundamental frequency of the speech signal, sets specific carrier parameters to shift the frequency of the speech signal to lower frequency, and then applies specific parameters and iterative methods to the VMD to decompose the speech signal in order to obtain the true IMFs that make up the speech signal. Through the decomposition experiments of real speech signals, it is demonstrated that VMD-FS solves the phenomenon of mode mixing and mode aliasing issues arising from the decomposition of speech signals compared with Empirical Mode Decomposition (EMD) and the original VMD method. From the Mean Square Error (MSE) of the decomposition results of the above three methods, it can be proved that VMD-FS outperforms EMD and VMD methods
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