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改进的频域盲分离排序不确定性消除算法 被引量：3

Improved Algorithm to Eliminate Permutation Indetermination of Frequency Domain Blind Source Separation
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摘要 讨论了频域盲源分离算法中的排序不确定性问题。根据相邻频点上同一个信号的频谱幅度相关大于不同信号频谱幅度相关的特点,提出了一个改进的基于相邻频点幅度相关性的排序不确定性消除算法。在两个相邻频点上构造一个幅度相关矩阵,再根据相关性大小将幅度相关矩阵转换为转置矩阵。该方法可以实时处理卷积信号频域盲源分离算法的排序不确定问题,在对每个频点进行独立分量分析的同时确定各分量的顺序。该算法效率高、鲁棒性好。仿真实验表明了算法的有效性。 The permutation problem in the frequency domain blind source separation was discussed. By utilizing the characteristic that amplitude correlation between neighbor bins of the same signal is better than different signals, an improved method based on the amplitude correlation between neighbor bins to eliminate the permutation indetermination was proposed. An amplitude correlation matrix between two neighbor bins was constructed, and it was transformed into a permutation matrix according to the value of amplitude correlation. This method can deal with the permutation problem of convolutive mixture blind separation in real time. It determines permutation of independent components （ICs） as soon as the independent component analysis algorithm is implemented at a frequency bin. This method is robust and fast. Simulation experiments demonstrate a good performance.
作者 王卫华 黄凤岗 王桐
机构地区 哈尔滨工程大学计算机科学与技术学院 哈尔滨工程大学信息与通信工程学院
出处 《系统仿真学报》 CAS CSCD 北大核心 2009年第2期496-499,共4页 Journal of System Simulation
关键词 频域盲源分离 卷积混合 排序不确定性 幅度相关 frequency domain blind source separation convolutive mixture permutation indetermination amplitude correlation
分类号 TN912.3 [电子电信—通信与信息系统]
  • 相关文献

参考文献11

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二级参考文献10

  • 1姜卫东,陆佶人,张宏滔.基于单个频点的水声信号盲源分离[J].电子与信息学报,2005,27(4):532-535. 被引量：5
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共引文献12

同被引文献23

  • 1肖明,谢胜利,傅予力.卷积混叠盲信号分离的一种线性化方法[J].华南理工大学学报（自然科学版）,2005,33(3):34-39. 被引量：3
  • 2姜卫东,陆佶人,张宏滔,高明生.基于相邻频点幅度相关的语音信号盲源分离[J].电路与系统学报,2005,10(3):1-4. 被引量：13
  • 3胡可,汪增福.一种基于时频分析的语音卷积信号盲分离算法[J].电子学报,2006,34(7):1246-1254. 被引量：12
  • 4WANG Lin, DING He-ping, YIN Fu-liang. A region-growing permutation alignment approach in frequency-domain blind source separation of speech mixtures [J]. IEEE Trans. on Audio, Speech, and Language Processing, 2011, 19(3) :549-557.
  • 5MEI T, MERTINS A, YIN F, et al. Blind source separation of convolutive mixtures based on the joint diagonaliza- tion of power spectral density matrices [J]. Signal Process, 2008,88(8) :1990-2007.
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  • 7SAWADA H, MUKAI R, ARAKI S, et al. A robust and precise method for solving the permutation problem of frequency-domain blind source separation [J]. IEEE Trans. on Speech Audio Processing, 2004,12(4) :530-538.
  • 8SARUWATARI H, NISHIKAWA T, KAWAMURA T, et el. Blind source separation on a fast-convergence algorithm combining ICA and beanfforming [J]. IEEE Trans on Audio, Speech and Language Processing,:2006,14 ( 2 ) :666 - 678.
  • 9CLARK F S P, PETRAGLIA M R, HADDAD D B. A new initialization method for frequency-domain blind source separation algorithm [ J ].IEEE Signal Processing Letters,2011,18(6): 343-346.
  • 10MURATA N, IKEDA S, ZIDHE A. An approach to blind sources sepa- ration based on temporal structure of speech signals [ J]. Neurocom- puting ,2001,41 ( 1 ) : 1-24.

引证文献3

二级引证文献4

系统仿真学报
2009年 第2期

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