Near-field localization algorithm of multiple sound sources based on approximated kernel density estimator

Yu-zhuo FANG; Zhi-yong XU; Zhao ZHAO

doi:10.11959/j.issn.1000-436x.2017013

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Near-field localization algorithm of multiple sound sources based on approximated kernel density estimator

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- Near-field localization algorithm of multiple sound sources based on approximated kernel density estimator
- Journal on Communications Vol. 38, Issue 1, Pages: 106-116(2017)
- 作者机构：
  
  南京理工大学电子工程与光电技术学院，江苏南京 210094
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61171167);The National Natural Science Foundation of China(61401203);The Natural Science Founda-tion of Jiangsu Province(BK20130776)
- DOI：10.11959/j.issn.1000-436x.2017013
  CLC： TN911.72
- Online First：2017-01，
  
  Published：25 January 2017
- 稿件说明：
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Yu-zhuo FANG, Zhi-yong XU, Zhao ZHAO. Near-field localization algorithm of multiple sound sources based on approximated kernel density estimator[J]. Journal on Communications, 2017, 38(1): 106-116.
DOI：

Yu-zhuo FANG, Zhi-yong XU, Zhao ZHAO. Near-field localization algorithm of multiple sound sources based on approximated kernel density estimator[J]. Journal on Communications, 2017, 38(1): 106-116. DOI： 10.11959/j.issn.1000-436x.2017013.

摘要

针对混响环境下的近场多声源定位问题，提出了一种基于近似核密度估计（KDE）的算法模型。引入多阶段（MS）分频带处理有效解决宽间距时的空域模糊，同时，构建空域似然率函数（SLF）通过相加（S）及相乘（P）2种算子进行多维融合，从而衍生出S-KDE、P-KDE、S-KDEMS和P-KDEMS 4种算法。通过对均方根误差（RMSE）以及表征辨识度的SLF百分比（PSLF）这2个统计指标的综合比较，证实了P-KDEMS是一种具有较高稳健性与辨识度的近场多声源定位算法。

Abstract

For near-field localization of multiple sound sources in reverberant environments

a algorithm model based on approximated kernel density estimator (KDE) was proposed.Multi-stage (MS) of sub-band processing was introduced to effectively solve the spatial aliasing by wide spacing.Spatial likelihood function (SLF) was built for multi-dimensional fusion by using two operators

sum (S) and prod (P).Then four algorithms

S-KDE

P-KDE

S-KDEMS

P-KDEMS

were derived.By the comprehensive comparison of the two statistical indicators root mean square error (RMSE) and percentage of SLF (PSLF) which denoted the recognition

P-KDEMS is confirmed as a near-field localization algorithm of multiple sound sources with high robustness and recognition.

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Keywords

references

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