Separation of vibration-derived sound signals based on fusion processing of vibration sensors and microphones

R. Takashima, Y. Kawaguchi, M. Togami
{"title":"Separation of vibration-derived sound signals based on fusion processing of vibration sensors and microphones","authors":"R. Takashima, Y. Kawaguchi, M. Togami","doi":"10.23919/EUSIPCO.2017.8081646","DOIUrl":null,"url":null,"abstract":"This paper proposes a sound source separation method for vibration-derived sound signals such as sounds derived from mechanical vibrations by using vibration sensors. The proposed method is based on two assumptions. First, a vibration signal and the sound derived from the vibration are assumed to have a linear correlation. This assumption enables us to model the vibration-derived sound as a linear convolution of a transfer function and a vibration signal recorded by a vibration sensor. Second, un-vibration-derived sound signals such that the sound source is not connected to vibration sensors via a solid medium are barely recorded by vibration sensors. This assumption leads to a constraint of the transfer function from the un-vibration-derived sound sources to the vibration sensors. The proposed framework is the same as a microphone-array-based blind source separation framework, except that the proposed method constructs arrays with microphones and vibration sensors, and the separation parameters are constrained by the prior knowledge gained from the above second assumption. Experimental results indicate that the separation performance of the proposed method is superior to that of a conventional microphone-array-based source separation method.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 25th European Signal Processing Conference (EUSIPCO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/EUSIPCO.2017.8081646","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

This paper proposes a sound source separation method for vibration-derived sound signals such as sounds derived from mechanical vibrations by using vibration sensors. The proposed method is based on two assumptions. First, a vibration signal and the sound derived from the vibration are assumed to have a linear correlation. This assumption enables us to model the vibration-derived sound as a linear convolution of a transfer function and a vibration signal recorded by a vibration sensor. Second, un-vibration-derived sound signals such that the sound source is not connected to vibration sensors via a solid medium are barely recorded by vibration sensors. This assumption leads to a constraint of the transfer function from the un-vibration-derived sound sources to the vibration sensors. The proposed framework is the same as a microphone-array-based blind source separation framework, except that the proposed method constructs arrays with microphones and vibration sensors, and the separation parameters are constrained by the prior knowledge gained from the above second assumption. Experimental results indicate that the separation performance of the proposed method is superior to that of a conventional microphone-array-based source separation method.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于振动传感器与传声器融合处理的振动声信号分离
本文提出了一种利用振动传感器分离机械振动声等振动源声信号的方法。该方法基于两个假设。首先,假设振动信号和由振动产生的声音具有线性相关关系。这个假设使我们能够将振动衍生的声音建模为传递函数和振动传感器记录的振动信号的线性卷积。其次,非振动产生的声音信号,即声源不通过固体介质连接到振动传感器,几乎不被振动传感器记录。这一假设导致了从非振动源到振动传感器的传递函数的约束。该框架与基于麦克风阵列的盲源分离框架相同,不同之处在于该方法构建了麦克风和振动传感器的阵列,并且分离参数受到由上述第二个假设获得的先验知识的约束。实验结果表明,该方法的分离性能优于传统的基于麦克风阵列的源分离方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Image deblurring using a perturbation-basec regularization approach Distributed computational load balancing for real-time applications Nonconvulsive epileptic seizures detection using multiway data analysis Performance improvement for wideband beamforming with white noise reduction based on sparse arrays Wideband DoA estimation based on joint optimisation of array and spatial sparsity
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1