一种稳健的室内无模糊多声源TDOA估计算法被引量：1

A Robust Algorithm for Unambiguous TDOA Estimation of Multiple Sound Sources under Indoor Environment

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摘要该文针对室内环境下的宽间距多声源到达时间差(TDOA)估计问题,研究了一种基于近似核密度估计(KDE)的无模糊算法。根据声频信号的短时频谱稀疏性,利用相关性检测(CT)提取单个声源能量占优的时频支撑域,进而将观测信号的归一化互功率谱(NCS)所构建的近似核函数通过累加平均削弱室内混响的干扰,同时引入多阶段(MS)分频带处理有效解决宽间距时的空域模糊。理论推导及仿真研究验证了该算法是一种稳健的室内无模糊多声源TDOA估计算法。 For Time Difference Of Arrival（TDOA） estimation of multiple sound sources with wide spacing under indoor environment, an unambiguous algorithm based on approximated Kernel Destiny Estimator（KDE） is studied. According to the short-time spectral sparseness of audio signals, the time-frequency bin with energy dominance of a single source is extracted from Coherence Test（CT）, then an approximated kernel function constructed of Normalized Cross-Spectrum（NCS） of obtained signals is used to weaken the interference of indoor reverberation with cumulative average, while adding Multi-Stage（MS） to divide the frequency band, the spatial ambiguity with wide spacing can be solved effectively. This algorithm is verified as an unambiguous TDOA estimation algorithm of multi-source under indoor environment by both theoretical derivation and simulation results.

作者房玉琢许志勇

机构地区南京理工大学电子工程与光电技术学院

出处《电子与信息学报》 EI CSCD 北大核心 2016年第5期1143-1150,共8页 Journal of Electronics & Information Technology

基金国家自然科学基金(61171167 61401203) 江苏省自然科学基金(BK20130776)~~

关键词语音信号处理麦克风阵列归一化互功率谱相关性检测近似核密度函数无模糊到达时间差估计 Speech signal processing Microphone array Normalized Cross-power Spectrum（NCS） Coherence Test（CT） Approximate kernel density function Unambiguous Time Difference Of Arrival（TDOA） estimation

分类号 TN912.3 [电子电信—通信与信息系统]

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参考文献15

1KNAPP C H and 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.
2TSIAMI A, KATSAMANIS A, MARAGOS P, et al. Experiments in acoustic source localization using sparse arrays in adverse indoors environments[C]. Proceedings of 2014 European Signal Processing Conference (EUSIPCO), Lisbon, Portugal, 2014: 2390-2394.
3张超,吴小培,吕钊.基于独立分量分析的运动目标检测算法中对通道数选择和观测向量生成方式的实验和分析[J].电子与信息学报,2015,37(1):137-142. 被引量：3
4LOMBARD A, ZHENG Y, BUCHNER H, et al. TDOA estimation for multiple sound sources in noisy and reverberant environments using broadband independent component analysis[J]. IEEE Transactions on Audio, Speech, and Language Processing, 2011, 19(6): 1490-1503.
5NESTA F, SVAIZER P, and OMOLOGO M. Cumulative state coherence transform for a robust two-channel multiple source localization[C]. Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation (ICA), Berlin, Germany, 2009: 290-297.
6NESTA F and 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.
7REDDY V V, KHONG W H, and 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.
8YILMAZ O and RICKARD S. Blind separation of speech mixtures via time-frequency masking[J]. IEEE Transactions on Signal Processing, 2004, 52(7): 1830-1847.
9ARAKI S, SAWADA H, MUKAI R, et al. DOA estimation for multiple sparse sources with normalized observation vector clustering[C]. Proceedings of 2006 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP 2006), Toulouse, France, 2006: 33-36.
10BRUTTI A and NESTA F. Tracking of multidimensional TDOA for multiple sources with distributed microphone pairs[J]. Computer Speech & Language, 2013, 27(3): 660-682.

二级参考文献35

1Knapp C H and Carter G C. The generalized correlation method for estimation of time delay[J]. IEEE Transactions on Acustics, Speech, and Signal Processing, 1976, 24(4): 320-327.
2Chen Jing-dong, Benesty J, and Huang Yi-teng. Performance of GCC-and AMDF-based time-delay estimation in practical reverberant environments[J]. EURASIP Journal on Applied Signal Processing, 2005, 2005(1): 25-36.
3Reed F A, Feintuch P L, and Bershad N J. Time delay estimation using the LMS adaptive filter - static behavior[J]. IEEE Transactions on Acustics, Speech, and Signal Processing, 1981, 29(3): 561-571.
4Yilmaz O, and Rickard S. Blind separation of speech mixtures via time-frequency masking[J]. IEEE Transactions on Signal Processing, 2004, 52(7): 1830-1847.
5Araki S, Sawada H, Mukai R, et al.. Underdetermined blind sparse source separation for arbitrarily arranged multiple sensors[J]. Signal Processing, 2007, 87(8): 1833-1847.
6Yermeche Z, Grbi C N, and Claesson I. Blind subband beamforming with time-delay constrmnts for moving source speech enhallcement[J]. IEEE Transactions on Audio, Speech, and Language Processing, 2007, 15(8): 2360-2372.
7JancoviccP , Zou Xin, and Kokuer M. Underdetermined DOA estimation via independent component analysis and time-frequency masking[J]. Journal of Electrical and Computer Engineering, 2010, Article ID 450487, 7 pages, doi: 10.1155/2010/450487.
8Kolossa D, Astudillo R F, Hoffmann E, et al.. Independent component analysis and time-frequency masking for speech recognition in multitalker conditions[J]. EURASIP Journal on Audio, Speech, and Music Processing, 2010, Article ID 651420, 13 pages, doi: 10.1155/2010/651420.
9Cobos M, Lopez J J, and Martinez D. Two-microphone multi-speaker localization based on a laplacian mixture model[J]. Digital Signal Processing, 2010, 21(1): 66-76.
10Ogasawara M, Nishino T, and Takeda K dodecahedral microphone array for blind A small source separation[C]. Proceedings of 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP 2001), Dallas, USA, 2010: 229-232.