An effective doa estimation by exploring the spatial sparse representation of the inter-sensor data ratio model

Y. Zou, Yifan Guo, Weiqiao Zheng, C. Ritz, J. Xi
{"title":"An effective doa estimation by exploring the spatial sparse representation of the inter-sensor data ratio model","authors":"Y. Zou, Yifan Guo, Weiqiao Zheng, C. Ritz, J. Xi","doi":"10.1109/ChinaSIP.2014.6889198","DOIUrl":null,"url":null,"abstract":"This paper investigates speaker direction of arrival (DOA) estimation using a single acoustic vector sensor (AVS). With the definition of the inter-sensor data ratio (ISDR) in the time-frequency (TF) domain and the use of the high local signal-to-noise ratio (HLSNR) TF points, an effective ISDR data model is derived, which determines the relationship between the ISDR and the AVS manifold vector. With the spatial sparse representation of the ISDR data, the DOA estimation is formulated by recovering the sparse matrix and locating the peak of the power spectrum of the reconstructed sparse matrix. Preliminary experimental results using simulations and real AVS recordings show that the proposed DOA estimation method is able to achieve high elevation and azimuth estimation accuracy for all angles when the SNR is above 10dB, avoiding the spatial aliasing problem and suppressing the adverse impact of the room reverberation. It is expected that the proposed DOA estimation method may find wide applications in portable devices due to its small compact physical size and superior performance.","PeriodicalId":248977,"journal":{"name":"2014 IEEE China Summit & International Conference on Signal and Information Processing (ChinaSIP)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE China Summit & International Conference on Signal and Information Processing (ChinaSIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ChinaSIP.2014.6889198","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

This paper investigates speaker direction of arrival (DOA) estimation using a single acoustic vector sensor (AVS). With the definition of the inter-sensor data ratio (ISDR) in the time-frequency (TF) domain and the use of the high local signal-to-noise ratio (HLSNR) TF points, an effective ISDR data model is derived, which determines the relationship between the ISDR and the AVS manifold vector. With the spatial sparse representation of the ISDR data, the DOA estimation is formulated by recovering the sparse matrix and locating the peak of the power spectrum of the reconstructed sparse matrix. Preliminary experimental results using simulations and real AVS recordings show that the proposed DOA estimation method is able to achieve high elevation and azimuth estimation accuracy for all angles when the SNR is above 10dB, avoiding the spatial aliasing problem and suppressing the adverse impact of the room reverberation. It is expected that the proposed DOA estimation method may find wide applications in portable devices due to its small compact physical size and superior performance.
通过探索传感器间数据比模型的空间稀疏表示,提出了一种有效的doa估计方法
本文研究了单声矢量传感器对说话人到达方向(DOA)的估计。通过定义时频域传感器间数据比(ISDR),并利用高局部信噪比(HLSNR)的TF点,推导了有效的ISDR数据模型,确定了ISDR与AVS流形向量的关系。利用ISDR数据的空间稀疏表示,通过恢复稀疏矩阵并定位重构稀疏矩阵的功率谱峰值来实现DOA估计。仿真和真实AVS录音的初步实验结果表明,在信噪比大于10dB的情况下,所提出的DOA估计方法在各个角度都能达到较高的仰角和方位估计精度,避免了空间混叠问题,抑制了室内混响的不利影响。该方法具有体积小、结构紧凑、性能优越等优点,有望在便携式设备中得到广泛应用。
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 求助全文
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:604180095
Book学术官方微信