基于近似核密度估计的近场多声源定位算法

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

通信学报 ›› 2017, Vol. 38 ›› Issue (1): 106-116.doi: 10.11959/j.issn.1000-436x.2017013

基于近似核密度估计的近场多声源定位算法

房玉琢,许志勇,赵兆

南京理工大学电子工程与光电技术学院，江苏南京 210094

修回日期:2016-11-30 出版日期:2017-01-01 发布日期:2017-01-23
作者简介:房玉琢（1987-），男，江苏南京人，南京理工大学博士生，主要研究方向为阵列信号处理、声学探测、盲信道辨识等。|许志勇（1968-），男，江苏南京人，博士，南京理工大学副教授，主要研究方向为阵列信号处理、声学探测、雷达技术等。|赵兆（1979-），男，湖北襄阳人，博士，南京理工大学副教授，主要研究方向为声探测系统与信号处理、时频分析。
基金资助:
国家自然科学基金资助项目(61171167);国家自然科学基金资助项目(61401203);江苏省自然科学基金资助项目(BK20130776)

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

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

School of Electronic and Optical Engineering,Nanjing University of Science and Technology,Nanjing 210094,China

Revised:2016-11-30 Online:2017-01-01 Published:2017-01-23
Supported by:
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)

摘要/Abstract

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。

关键词: 麦克风阵列, 近似核密度估计, 多阶段分频带处理, 空域似然率函数, 数据融合

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.

Key words: microphone array, approximated kernel density estimator, multi-stage of sub-band processing, spatial likeli-hood function, data fusion

中图分类号:

TN911.72

房玉琢,许志勇,赵兆. 基于近似核密度估计的近场多声源定位算法[J]. 通信学报, 2017, 38(1): 106-116.

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.

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参考文献 16

[1]	WU K , KHONG A W H . Sound source localization and tracking[M]// Context Aware Human-Robot and Human-Agent Interaction. Springer International Publishing, 2016: 55-78.
[2]	KNAPP C H , CARTER G C . The generalized correlation method for estimation of time delay[J]. IEEE Transactions on Acoustics,Speech,and Signal Processing, 1976,24(4): 320-327.
[3]	BRANDSTEIN M S , SILVERMAN H F . A practical methodology for speech source localization with microphone arrays[J]. Computer Speech and Language, 1997,11(2): 91-126.
[4]	RABINKIN D V , RANOMERON R J , DAHL A ,et al. A DSP implementation of source location using microphone arrays[J]. Journal of the Acoustical Society of America, 1996,99(4): 88-99.
[5]	WARD D B , WILLIAMSON R C . Particle filter beamforming for acoustic source localization in a reverberant environment[C]// 2002 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP). Orlando,USA, 2002: 1777-1788.
[6]	DIBIASE J H . A high-accuracy,low-latency technique for talker localization in reverberant environments using microphone arrays[D]. Brown University, 2000: 73-105.
[7]	PERTIL? P , KORHONEN T , VISA A . Measurement combination for acoustic source localization in a room environment[J]. EURASIP Journal on Audio Speech and Music Processing, 2008,2008: 1-14.
[8]	TSIAMI A , KATSAMANIS A , MARAGOS P ,et al. .Experiments in acoustic source localization using sparse arrays in adverse indoors environments[C]// 2014 European Signal Processing Conference (EUSIPCO). Lisbon,Portugal, 2014: 2390-2394.
[9]	XU Z Y , ZHAO Z , LIU M . Real-time unambiguous passive direction finding for multiple sound sources with widely spaced microphone array[J]. Journal of Electronics ＆ Information Technology, 2011,33(9): 2056-2061.
[10]	NESTA F , OMOLOGO M . Generalized state coherence transform for multidimensional TDOA estimation of multiple sources[J]. IEEE Transactions on Audio,Speech,and Language Processing, 2012,20(1): 246-260.
[11]	BRUTTI A , NESTA F . Tracking of multidimensional TDOA for multiple sources with distributed microphone pairs[J]. Computer Speech and Language, 2013,27(3): 660-682.
[12]	YILMAZ O , RICKARD S . Blind separation of speech mixtures via time-frequency masking[J]. IEEE Transactions on Signal Processing, 2004,52(7): 1830-1847.
[13]	REDDY V V , KHONG W H , NG B P . Unambiguous speech DOA estimation under spatial aliasing conditions[J]. IEEE Transactions on Audio,Speech,and Language Processing, 2014,22(12): 2133-2145.
[14]	MOHAN S , LOCKWOOD M E , KRAMER M L ,et al. Localization of multiple acoustic sources with small arrays using a coherence test[J]. Journal of the Acoustical Society of America, 2008,123(4): 2136-2147.
[15]	GUSTAFFSON T , RAO B D , TRIVEDI M . Source localization in reverberant environments:modeling and statistical analysis[J]. IEEE Transactions on Speech and Audio Processing, 2003,11(6): 791-803.
[16]	LEHMANN E and JOHANSSON A . Prediction of energy decay in room impulse responses simulated with an image-source model[J]. Journal of the Acoustical Society of America, 2008,124(1): 269-277.

基于近似核密度估计的近场多声源定位算法

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

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