基于移频的语音信号变分模分解方法

Wenyang Liu, Weiping Hu, Deli Fu
{"title":"基于移频的语音信号变分模分解方法","authors":"Wenyang Liu, Weiping Hu, Deli Fu","doi":"10.1109/ICARCE55724.2022.10046652","DOIUrl":null,"url":null,"abstract":"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","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Frequency Shifting-based Variational Mode Decomposition Method for Speech Signal Decomposition\",\"authors\":\"Wenyang Liu, Weiping Hu, Deli Fu\",\"doi\":\"10.1109/ICARCE55724.2022.10046652\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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\",\"PeriodicalId\":416305,\"journal\":{\"name\":\"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)\",\"volume\":\"47 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICARCE55724.2022.10046652\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICARCE55724.2022.10046652","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

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
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信