基于联合（块）对角化的盲分离算法的研究
|
摘要
盲源分离是在源信号与传输信道未知的情况下仅利用接收天线的观测数据提取或恢复统计独立的源信号。盲源分离具有非常重要的理论意义和实用价值,被广泛应用于无线通信领域、生物医学信号分析与处理、图像识别、数据挖掘、语音增强以及地球物理数据处理等方面,具有非常广阔的发展前景,极大地引发了信号处理和神经网络等学科众多研究者的共同关注。本文紧密围绕这一热点课题展开,并重点研究基于联合(块)对角化的盲分离算法。联合对角化(Joint Diagonalization,JD)和联合块对角化(Joint Block-Diagonalization, JBD)分别是解决瞬时和卷积混叠盲分离的重要技术,根据这两条主线,本文的主要工作可归纳如下:
1.给出具有广泛适用性的非正交JD可辨识性定理,为基于非正交JD的盲分离算法提供了理论依据。用代数方法严格证明了,对于由共同的非正交(酉)混叠矩阵构成的具有可联合对角化结构的一组目标矩阵,从中提取(估计)出的混叠矩阵与真实的混叠矩阵本质相等,即仅相差一个广义置换矩阵。从而为基于非正交JD的盲分离算法提供了理论依据。
2.根据盲分离算法固有的尺度和排列不定性,对经典的基于拟合误差最小二乘代价函数进行改进,提出三二次拟合代价函数,使原函数中关于混叠矩阵的四次函数转化为三组待定参数的二次函数。同时,给出了优化该代价函数的基于三二次迭代的非正交JD算法(Tri-quadratic Iterative Algorithm,TIA),该算法既不要求目标矩阵为对称或是共轭对称,也不要求混叠矩阵为(酉)正交矩阵。引入动力学理论中的李雅普诺夫函数以及LaSalle’s不变原则, 1)论证了在理想条件下,TIA算法收敛到三二次拟合函数的不变集; 2)验证了盲分离问题的可接受集合属于三二次拟合函数的不变集。
3.提出一种基于二次优化的正交JBD方法:QJBD,该方法以非对角线上各子矩阵F-范数的平方和作为联合块对角化性能的评判准则,将原四次代价函数转化为一组较为简单的带约束的二次子代价函数,每一子代价函数用于估计酉分离矩阵的一个子矩阵。依次最小化各子函数,迭代搜索代价函数最小点,得到分离矩阵的估计。理论分析和实验结果表明,该方法收敛速度快,可直接在时域进行卷积盲分离;同时,该方法在不降低分离性能的基础上,克服了传统的基于类Jacobi旋转的JBD方法对于传输信道阶数和初始值较敏感的缺陷。
4.研究表明,将适用于非正交JD的J-Di算法直接用于JBD所得到的分离矩阵,与真实的JBD分离矩阵之间,只相差一个置换矩阵。基于此,本文提出一种基于改进J-Di算法的非正交JBD方法:GH-NOJBD,直接在时域解决卷积盲分离问题。该方法不需要对观测信号进行预白化处理,从而不再要求至少存在一个目标矩阵为正定矩阵,也消除了白化误差的影响,具有比基于类Jacobi旋转的JBD方法更好的分离性能。
5.分析接收信号相关矩阵组具有三因子(三矩阵)线性相乘的联合块对角化结构,通过对相关矩阵组构成的三维矩阵进行切片分割,并对切片矩阵各元素重新排列规划,得到关于三组待定矩阵的标准最小二乘解。进而提出了一种基于交替最小二乘的三因子分解算法(记作ALS-NOJBD),联合估计待定矩阵的所有参数,实现非正交JBD,克服了现有的非正交ZJBD算法易于产生奇异解以及本文所提出的GH-NOJBD算法对噪声敏感的不足。
6.受到适用于非正交JD的TIA算法的启发,提出一种具有普遍适用性的基于三迭代的非正交JBD算法:TIA-NOJBD。与ALS-NOJBD算法相比,TIA-NOJBD继承了ALS-NOJBD收敛性能稳定的优点,同时避免了高阶矩阵运算,使得计算复杂度降低了至少一个数量级。与现有的(非)正交JBD算法(包括本文所提的QJBD,GH-NOJBD,ALS-NOJBD算法)相比,TIA-NOJBD在适用条件、计算复杂度和收敛性能这三个方面都更具优势。
7.提出一种基于单分量追踪的多阶段时频卷积盲分离方法,MTD。该方法每次只分离一路信号的所有频率分量,有效地避免了由于单独处理各个频点而引入的频间排列不定性。该方法可认为是基于联合对角化的瞬时盲分离算法在时频域卷积盲分离问题中的推广应用。
Blind source separation (BSS) aims to extract independent but unobservered source signals from their mixtures captured by a number of sensors without knowing the mixing coefficients. In the past two decades, BSS has received much attention in various fields, such as speech and audio processing, image enhancement and biomedical signal processing. This dissertation emphasizes on the theory and algorithm of BSS in both instantaneous case and convolutive case. The joint diagonalization (JD) and joint block-diagonalization (JBD) are the essential tools to resolve the instantaneous BSS and convolutive BSS, respectively. Accordingly, the main contributions of this dissertation are summarized as follows:
1. An identifiability theorem of non-orthogonal (non-unitary) JD was given with wide applicability, which provides a theoretical basis for non-orthogonal (non-unitary) JD based BSS algorithms. It was shown with a strict algebraic proof that the extracted mixing matrix from a group of objective matrices constructed from a common mixing matric, is essentially equal to the real mixing matrix, i.e., there is only a difference of one generalized permutation matrix between the extracted and the real mixing matrices.
2. Based on the permutation and scaling indeterminacies for the BSS problems, a novel tri-quadratic fitting function was proposed by improving the classical least squares cost function. The new fitting function is quadratic if any two of the three unknown parameter sets are fixed. Meanwhile, the corresponding non-orthogonal JD algorithm, tri-quadratic iterative algorithm (TIA), was given to optimize the new quadratic fitting function. Neither the (conjugate) symmetry of the objective matrix nor the orthogonality of the mixing function is required in the TIA algorithm. Convergence of TIA algorithm was analyzed by utilizing the definition of the Lyapunov function and the LaSalle’Invariance Principle. The analytical results indicate that the TIA converges to the invariant set of the tri-quadratic fitting function that contains the acceptable set of the BSS.
3. Taking the sum of the F-norms of all off-diagonal sub-matrices as a criterion, a novel orthogonal JBD algorithm, QJBD (quadratic JBD), was proposed to estimate the whole mixing matrix through minimizing a sequence of restrictive quadratic subfunctions corresponding to mixing submatrices. Both theoretical analysis and simulation results show that QJBD has much lower complexity and faster convergence speed than that of the classical Jacobi-like methods without performance loss. Furthermore, different from Jacobi-like methods, QJBD is not sensitive to the channel order and initialization values.
4. Analysis shows that there is only a difference of one permutation matrix between the estimated and the real separation matrices, if the J-Di algorithm that is used traditionally for non-orthogonal JD, is directly applied to JBD. Accordingly, a non-orthogonal JBD algorithm, GH-NOJBD, was proposed by improving the J-Di algorithm. GH-NOJBD solves the convolutive-mixture BSS directly in time domain and does not require pre-whitening of the observed signals. Then, obviously there is no extra error introduced by pre-whitening and as a result, there is no need to require at least one objective matrix to be positive definite. What is the most important is that the separation performance of the proposed GH-NOJBD algorithm is superior to that of the Jacobi-like JBD counterparts.
5. To overcome the disadvantages of the existing ZJBD algorithm (easily generating singular solutions) and the proposed GH-NOJBD algorithm (sensitive to noise), an alternating and iterative approach (ALS-NOJBD) for non-orthogonal JBD was proposed to estimate all the parameters of the undetermined matrix jointly. Because the correlation matrix group of the received signals has a three-factor-product JBD structure, standard least-squares expressions could be deduced with respect to the three factorable matrices by cutting pieces and then reprogramming of the 3-D matrix composed of the correlation matrix group. This is just the motivation of the ALS-NOJBD algorithm. Simulation results show that compared with traditional JBD algorithms, ALS-NOJBD has better and more stable separation performance without limitation in initial parameter choosing, and effectively overcome the disadvantages of both ZJBD and GH-NOJBD algorithms.
6. Inspired by the TIA for JD, its counterpart for JBD, TIA-NOJBD algorithm was proposed. In each sub-step, a closed solution is derived by minimizing the cost function associated with one parameter-group while fixing the others. Compared with ALS-NOJBD, TIA-NOJBD also has a stable separation performance but at least one order of magnitude lower computation complexity. Besides, TIA-NOJBD is superior to all other JBD algorithms (including those proposed in this thesis, QJBD,GH-NOJBD,ALS-NOJBD) in three aspects, i.e., application conditions, computation complexity and convergency performance.
7. A multistage decomposition based on single component tracking (MTD) was proposed to resolve the convolutive BSS in time-frequency domain. Unlike the previous frequency-domain methods, MTD estimates one set of inverse FIR parameters corresponding to only one of the separated signals at a time. This feature of MTD makes there are no internal-permutation and internal-scaling ambiguities which are the inherent disadvantages of the frequency-domain convolutive BSS algorithms. From a certain point of view, MTD can be considered as an extential form of JD in time-frequency domain.
1.给出具有广泛适用性的非正交JD可辨识性定理,为基于非正交JD的盲分离算法提供了理论依据。用代数方法严格证明了,对于由共同的非正交(酉)混叠矩阵构成的具有可联合对角化结构的一组目标矩阵,从中提取(估计)出的混叠矩阵与真实的混叠矩阵本质相等,即仅相差一个广义置换矩阵。从而为基于非正交JD的盲分离算法提供了理论依据。
2.根据盲分离算法固有的尺度和排列不定性,对经典的基于拟合误差最小二乘代价函数进行改进,提出三二次拟合代价函数,使原函数中关于混叠矩阵的四次函数转化为三组待定参数的二次函数。同时,给出了优化该代价函数的基于三二次迭代的非正交JD算法(Tri-quadratic Iterative Algorithm,TIA),该算法既不要求目标矩阵为对称或是共轭对称,也不要求混叠矩阵为(酉)正交矩阵。引入动力学理论中的李雅普诺夫函数以及LaSalle’s不变原则, 1)论证了在理想条件下,TIA算法收敛到三二次拟合函数的不变集; 2)验证了盲分离问题的可接受集合属于三二次拟合函数的不变集。
3.提出一种基于二次优化的正交JBD方法:QJBD,该方法以非对角线上各子矩阵F-范数的平方和作为联合块对角化性能的评判准则,将原四次代价函数转化为一组较为简单的带约束的二次子代价函数,每一子代价函数用于估计酉分离矩阵的一个子矩阵。依次最小化各子函数,迭代搜索代价函数最小点,得到分离矩阵的估计。理论分析和实验结果表明,该方法收敛速度快,可直接在时域进行卷积盲分离;同时,该方法在不降低分离性能的基础上,克服了传统的基于类Jacobi旋转的JBD方法对于传输信道阶数和初始值较敏感的缺陷。
4.研究表明,将适用于非正交JD的J-Di算法直接用于JBD所得到的分离矩阵,与真实的JBD分离矩阵之间,只相差一个置换矩阵。基于此,本文提出一种基于改进J-Di算法的非正交JBD方法:GH-NOJBD,直接在时域解决卷积盲分离问题。该方法不需要对观测信号进行预白化处理,从而不再要求至少存在一个目标矩阵为正定矩阵,也消除了白化误差的影响,具有比基于类Jacobi旋转的JBD方法更好的分离性能。
5.分析接收信号相关矩阵组具有三因子(三矩阵)线性相乘的联合块对角化结构,通过对相关矩阵组构成的三维矩阵进行切片分割,并对切片矩阵各元素重新排列规划,得到关于三组待定矩阵的标准最小二乘解。进而提出了一种基于交替最小二乘的三因子分解算法(记作ALS-NOJBD),联合估计待定矩阵的所有参数,实现非正交JBD,克服了现有的非正交ZJBD算法易于产生奇异解以及本文所提出的GH-NOJBD算法对噪声敏感的不足。
6.受到适用于非正交JD的TIA算法的启发,提出一种具有普遍适用性的基于三迭代的非正交JBD算法:TIA-NOJBD。与ALS-NOJBD算法相比,TIA-NOJBD继承了ALS-NOJBD收敛性能稳定的优点,同时避免了高阶矩阵运算,使得计算复杂度降低了至少一个数量级。与现有的(非)正交JBD算法(包括本文所提的QJBD,GH-NOJBD,ALS-NOJBD算法)相比,TIA-NOJBD在适用条件、计算复杂度和收敛性能这三个方面都更具优势。
7.提出一种基于单分量追踪的多阶段时频卷积盲分离方法,MTD。该方法每次只分离一路信号的所有频率分量,有效地避免了由于单独处理各个频点而引入的频间排列不定性。该方法可认为是基于联合对角化的瞬时盲分离算法在时频域卷积盲分离问题中的推广应用。
Blind source separation (BSS) aims to extract independent but unobservered source signals from their mixtures captured by a number of sensors without knowing the mixing coefficients. In the past two decades, BSS has received much attention in various fields, such as speech and audio processing, image enhancement and biomedical signal processing. This dissertation emphasizes on the theory and algorithm of BSS in both instantaneous case and convolutive case. The joint diagonalization (JD) and joint block-diagonalization (JBD) are the essential tools to resolve the instantaneous BSS and convolutive BSS, respectively. Accordingly, the main contributions of this dissertation are summarized as follows:
1. An identifiability theorem of non-orthogonal (non-unitary) JD was given with wide applicability, which provides a theoretical basis for non-orthogonal (non-unitary) JD based BSS algorithms. It was shown with a strict algebraic proof that the extracted mixing matrix from a group of objective matrices constructed from a common mixing matric, is essentially equal to the real mixing matrix, i.e., there is only a difference of one generalized permutation matrix between the extracted and the real mixing matrices.
2. Based on the permutation and scaling indeterminacies for the BSS problems, a novel tri-quadratic fitting function was proposed by improving the classical least squares cost function. The new fitting function is quadratic if any two of the three unknown parameter sets are fixed. Meanwhile, the corresponding non-orthogonal JD algorithm, tri-quadratic iterative algorithm (TIA), was given to optimize the new quadratic fitting function. Neither the (conjugate) symmetry of the objective matrix nor the orthogonality of the mixing function is required in the TIA algorithm. Convergence of TIA algorithm was analyzed by utilizing the definition of the Lyapunov function and the LaSalle’Invariance Principle. The analytical results indicate that the TIA converges to the invariant set of the tri-quadratic fitting function that contains the acceptable set of the BSS.
3. Taking the sum of the F-norms of all off-diagonal sub-matrices as a criterion, a novel orthogonal JBD algorithm, QJBD (quadratic JBD), was proposed to estimate the whole mixing matrix through minimizing a sequence of restrictive quadratic subfunctions corresponding to mixing submatrices. Both theoretical analysis and simulation results show that QJBD has much lower complexity and faster convergence speed than that of the classical Jacobi-like methods without performance loss. Furthermore, different from Jacobi-like methods, QJBD is not sensitive to the channel order and initialization values.
4. Analysis shows that there is only a difference of one permutation matrix between the estimated and the real separation matrices, if the J-Di algorithm that is used traditionally for non-orthogonal JD, is directly applied to JBD. Accordingly, a non-orthogonal JBD algorithm, GH-NOJBD, was proposed by improving the J-Di algorithm. GH-NOJBD solves the convolutive-mixture BSS directly in time domain and does not require pre-whitening of the observed signals. Then, obviously there is no extra error introduced by pre-whitening and as a result, there is no need to require at least one objective matrix to be positive definite. What is the most important is that the separation performance of the proposed GH-NOJBD algorithm is superior to that of the Jacobi-like JBD counterparts.
5. To overcome the disadvantages of the existing ZJBD algorithm (easily generating singular solutions) and the proposed GH-NOJBD algorithm (sensitive to noise), an alternating and iterative approach (ALS-NOJBD) for non-orthogonal JBD was proposed to estimate all the parameters of the undetermined matrix jointly. Because the correlation matrix group of the received signals has a three-factor-product JBD structure, standard least-squares expressions could be deduced with respect to the three factorable matrices by cutting pieces and then reprogramming of the 3-D matrix composed of the correlation matrix group. This is just the motivation of the ALS-NOJBD algorithm. Simulation results show that compared with traditional JBD algorithms, ALS-NOJBD has better and more stable separation performance without limitation in initial parameter choosing, and effectively overcome the disadvantages of both ZJBD and GH-NOJBD algorithms.
6. Inspired by the TIA for JD, its counterpart for JBD, TIA-NOJBD algorithm was proposed. In each sub-step, a closed solution is derived by minimizing the cost function associated with one parameter-group while fixing the others. Compared with ALS-NOJBD, TIA-NOJBD also has a stable separation performance but at least one order of magnitude lower computation complexity. Besides, TIA-NOJBD is superior to all other JBD algorithms (including those proposed in this thesis, QJBD,GH-NOJBD,ALS-NOJBD) in three aspects, i.e., application conditions, computation complexity and convergency performance.
7. A multistage decomposition based on single component tracking (MTD) was proposed to resolve the convolutive BSS in time-frequency domain. Unlike the previous frequency-domain methods, MTD estimates one set of inverse FIR parameters corresponding to only one of the separated signals at a time. This feature of MTD makes there are no internal-permutation and internal-scaling ambiguities which are the inherent disadvantages of the frequency-domain convolutive BSS algorithms. From a certain point of view, MTD can be considered as an extential form of JD in time-frequency domain.
引文
[1] T. W. Lee, Independent component analysis: theory and application. Dordrecht: Kluwer Academic Publishers, 1998.
[2] S. Haykin, Unsupervised adaptive filtering, vol. 1. New York: Wiley, 2000.
[3] A. Hyvarinen, J. Karhunen, and E. Oja, Independent Conponent Anaylysis. New York: Wiley, 2001.
[4]张贤达,保铮,通信信号处理:国防工业出版社, 2002.
[5] P. Georgiev and A. CIchocki, "Robust independent component analysis via time-delay cumulant function," IEICE Trans. on Fundamentals, 2003, E86-A (3), pp: 573-580.
[6] J. V. Stone, Independent Component Analysis: MIT Press, 2004.
[7]杨福生,洪波,独立分量分析的原理与应用:清华大学出版社, 2006.
[8] S. Makino, T. W. Lee, and H. Sawada, Blind speech separation: Springer, 2007.
[9]史习智,盲信号处理-理论与实践:上海交通大学出版社, 2008.
[10] J. K. Tugnait, "Identification and deconvolution of multichannel linear non-Gaussian processes using higher-order statistices and inverse filter criteria," IEEE Trans on Signal Processing, 1997, 45 (3), pp: 658-672.
[11] S. C. Douglas, "Fixed-point algorithms for the blind separation of arbitrary complex-valued non-Gaussians signal mixtures," EURASIP Journal on advances in Signal Processing, 2007, 1 (3), pp: 1-15.
[12] J. F. Cardoso and A. Souloumiac, "Blind beamforming for non-Gaussian signals," IEE Proceedings-F, 1993, 140 (6), pp: 362-370.
[13] A. Souloumiac, "Blind source detection and separation using second order non-stationarity," Proc. IEEE Acoustics, Speech and Signal Processing, 1995, pp: 1912-1915.
[14] A. Hyvarinen, "Blind source separation by nonstationarity of variance: a cumulant-based approach," IEEE Trans on Neural Networks, 2001, 12 (6), pp: 1471-1474.
[15] F. L. Yin, T. M. Mei, and J. Wang, "Blind source separation based on decorrelation and nonstationarity," IEEE trans on Circuits and Systems-I: Regular Papers, 2007, 54 (5), pp: 1150-1158.
[16] L. Molgedey and H. G. Schuster, "Separation of a mixture of independent signals using time delayed correlations," Phyical Review Letters, 1994, 72 (23), pp: 3634-3637.
[17] S. Amari and A. Cichocki, "Adaptive blind signal processing-neural network aprroachs," Proceedings of the IEEE, 1998, 86 (10), pp: 2026-2048.
[18] J.-F. Cardoso, "Blind signal separation: statistical principle " Proceedings of the IEEE, 1998, 86 (10), pp: 2009-2025.
[19] H. H. Yang, "Serial updating rule for blind separation derived from the method of scoring," IEEE Signal Processing Letters, 1999, 47 (8), pp: 2275-2285.
[20] A. J. Bell and T. J. Sejnowski, "An information maximization approach to blind separation and blind deconvolution," Neural Computaiton, 1995, 7 (6), pp: 1129-1159.
[21] S. Amari, A. Cichocki, and H. Yang, "A new learning algorithm for blind signal separation.Advances in Neural Information Processing Systems," MA, 1996.
[22] J. F. Cardoso, "Infomax and maximum likelihood for blind source separation," IEEE Signal Processing Letters, 1997, 4, pp: 112-114.
[23] J. Karhunen and J. Joutsensal, "Representation and separation of signals using nonlinear PCA type learing," Neural Networks, 1994, 7 (1), pp: 113-127.
[24] C. L. Nikias and A. P. Petropulu, Higher-order spectra analysis: a nonlinear signal processing framwork. Nnglewood Cliffs: Prentice-Hall, 1993.
[25] Y. Tan and J. Wang, "Nonlinear blind source separation using higher order statistics and a genetic algorithm," IEEE Trans on Evolutionary Computation, 2001, 5 (6), pp: 600-612.
[26] J.-F. Cardoso, "Super-symmetric decompositon of the forth-order cumulant tensor, blind identification of more sources than sensors," Proc. IEEE Proceedings of international conference on Acoustic, Speech and Signal Processing (ICASSP'91), 1991, pp: 3109-3112.
[27] J.-F. Cardoso, "Source separation usign higher order moment," IEEE Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP'89), 1989, (4), pp: 2109-2112.
[28] C. Simon, P. Loubaton, C. Vignat, C. Jutten, and G. d'Urso, "Blind source separation of convolutive mixtures by maximization of fourth-order cumulants: the non i.d.d.case," Proceedings of the 32th Asilomar Conference on Signals, Systems&Computers, 1998, (2), pp: 1584-1588.
[29] L. De-Lathauwer, B. De-Moor, and J. Vandewalle, "Independent components analysis and (simultaneous) third-order tensor diagonalization," IEEE Trans on Signal Processing, 2001, 49 (10), pp: 2262-2271.
[30] X. L. LI and X. D. Zhang, "Sequential blind extraction adopting Second-order statistics," IEEE Signal Processing Letters, 2006, 14 (1), pp: 58-61.
[31] J.-F. Cardoso and B. H. Laheld, "Equivariant adaptive source separation," IEEE Trans on Signal Processing, 1996, 44 (12), pp: 3017-3030.
[32] A. Hyvarinen, "Fast and robust fixed-point algorithms for independent component analysis," IEEE Trans on Neural Networks, 1999, 10 (3), pp: 626-634.
[33] V. Zarzoso and A. K. Nandi, "Blind separation of independent sources for virtually any source probability density function " IEEE Trans on Signal Processing, 1999, 49 (9), pp: 2419-2432.
[34] V. Zarzoso and A. K. Nandi, "Adaptive blind source separation for virtually any source probability density function " IEEE Trans on Signal Processing, 2000, 48 (2), pp: 477-489.
[35] D. Yellin and E. Weinstein, "Criteria for multichannel signal separation," IEEE Trans on Signal Processing, 1994, 42 (8), pp: 2156-2168.
[36] D. Yellin and E. Weinstein, "Multichannel signal separation: methods and analysis," IEEE Trans on Signal Processing, 1996, 44 (1), pp: 106-118.
[37] P. Comon, "Independent component analysis: A new concept?" Signal Processing, 1994, 36 (3), pp: 287-314.
[38] A. Hyvarinen and E. Oja, "A fast fixed-point algorithm for independent component analysis," Neural Computaiton, 1997, 9, pp: 1483-1492.
[39] Z. Ding and T. Nguyen, "Stationary points of a kurtosis maximization algorithm for blind signal separation and antenna beamforming," IEEE Trans on Signal Processing, 2000, 48 (6), pp: 1587-1596.
[40] S. Choi, A. Cichocki, and A. Belouchrani, "Blind separation of second-order nonstationary and temporally colored sources," Proceedings of the 11th IEEE signal processing workshop, 2001, pp: 444-447.
[41] M. Kawamoto and Y. Inouye, "Blind deconvolution of MIMO-FIR systems with colored inputs using second order statistices," IEICE Trans. on Fundamentals, 2003, E86-A (3), pp: 597-604.
[42] A. Belouchrani, K. Abed-Meraim, J.-F. Cardoso, and E. Moulines, "A blind source separation technique using second-order statistics," IEEE Trans on Signal Processing, 1997, 45 (2), pp: 433-443.
[43] R. Zhang and M. K. Tsatsanis, "A second-order method for blind separaiton of non-stationary sources," IEEE Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP'01), 2001, (5), pp: 2729-2800.
[44] N. Delfosse and P. Loubaton, "Adaptive blind separation of independent sources : A second-order stable algorithm for the general case," IEEE Trans On Circuits and Systems-I: Fundamental Theory and Applications, 2000, 47 (7), pp: 1057-1071.
[45] U. A. Lindgren and H. Broman, "Source separation using a criterion beas onsecond-order statistices," IEEE Trans on Signal Processing, 1998, 46 (7), pp: 1837-1850.
[46] Y. Xiang, K. Abed-Meraim, and Y. Hua, "Adaptive blind source separatio by second order statistics and natural gradient," IEEE Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP'99), 1999, pp: 2917-2920.
[47] H. Buchner, R. Aichner, and W. Kellermann, "A generalization of blind source separation algorithm for convolutive mixtures based on second-order statistics," IEEE Trans on Speech and Audio Processing, 2005, 13 (1), pp: 120-134.
[48] L. Tong, R. Liu, V. C. Soon, and Y.-F. Huang, "Indeterminacy and identifiability of blind identification," IEEE Trans on Circuits and Systems, 1991, 38 (5), pp: 499-509.
[49] L. Tong and R. Liu, "Blind estimation of correlated source signals," Proceedings of ACSSC, 1990, (1), pp: 258-262.
[50] C. Chang, Z. Ding, S. F. Yau, and D. H. Y. Chan, "A matrix-pencil approach to blind separation of colored non-stationary signals," IEEE Signal Processing Letters, 2000, 7 (12), pp: 348-350.
[51] K. Matsuoka, M. Ohya, and M. Kawamoto, "A neural net for blind separation of nonstationary signals," Neural Computaiton, 1995, 8 (3), pp: 411-419.
[52] S. Choi, A. Cichocki, and S. Amari, "Equivariant nonstationary source separation," Neural Computaiton, 2002, 15, pp: 121-130.
[53] J. Herault and C. Jutten, "Space or time adaptive signal processing by neural network models," Proc. Neural Network for Computing: AIP Conference Proceeding, New York, 1986.
[54] C. Jutten and J. Herault, "Blind separation of soueces, Part I: an adaptive algorithm based on neuromimetic architecture," Signal processing, 1991, 24, pp: 1-10.
[55] P. Commo, C. Jutten, and J. Herault, "Blind separation of sources, Part II: Problems statement," Signal processing, 1991, 24, pp: 1-10.
[56] G. B. Giannakis and A. Swami, "New results on state-space and input-outpu identification of non-Gaussian processing using cumulants," Proc. SPIE'87, San Diego, C A, 1987, pp: 199-205.
[57] E. Sorouchyari, "Blind source separation, Part II: stability analysis," Signal processing, 1991, 24, pp: 21-29.
[58] A. Cichocki, R. Unbehauen, L. Moszczynski, and E. Rummaert, "A new on-line adaptive learning algorithm for blind separation of source signals," Proc. International Symposium on Artificial Nerual Network (ISANN'94), Taiwan, 1994, pp: 406-411.
[59] E. Oja, J. Karhunen, L. Wang, and R. Vigario, "Principle and independent componentsin neural networks-recent developments," Proc. The 7th Italian Workshop Neural Networks (WIRN'95), Vietri, Italy, 1995, pp: 20-26.
[60] N. Delfosse and P. Loubaton, "Adaptive separation of independent sources: a deflation approach," IEEE Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP'94), 1994, IV, pp: 41-44.
[61] N. Delfosse and P. Loubaton, "Adaptive blind separatio of independent sources: a deflation approach," Signal processing, 1995, 45 (7), pp: 59-83.
[62] A. Cichocki and R. Unbehauen, "Robust neural networks with on-line learning for blind identification and blind separation," IEEE Trans on Circuits and Systems-I, 1996, 43 (11), pp: 894-906.
[63] A. Amari, "Natural gradient works efficiently in learning," Neural Computaiton, 1998, (10), pp: 251-276.
[64] A. Cichocki and A. Amari, Adaptive blind signal and image processing. New York: Wiley, 2003.
[65] H. H. Yang and A. Amari, "Adaptive on-line learning algorithms for blind separation maximum entropy and minimum mutual infromation," Neural Computaiton, 1997, 9 (7), pp: 1457-1482.
[66] X. R. Cao and R. W. Liu, "General approach to blind source separation," IEEE Trans on Signal Processing, 1996, 44 (3), pp: 562-571.
[67] A. Amari and J. F. Cardoso, "Blind source separation: semi-parametric statistical approach," IEEE Trans on Signal Processing, 1997, 45 (11), pp: 2692-2700.
[68] J. F. Cardoso, "On the stability of source separation algorithms," Jouranl of VLSI Signal Processing, 2000, 26 (1/2), pp: 7-23.
[69] S. Amari, S. C. Douglas, A. Cichocki, and H. H. Yang, "Multichannel blind deconvolution and equalization using the natural gradient," IEEE Proceedings of International Workshop Wireless Communication, 1997, pp: 101-104.
[70] A. Belouchrani and A. Cichocki, "A robust whitening procedure in blind source separation context," Electronics Letters, 2000, 36 (24), pp: 2050-2053.
[71] D. Z. Feng, W. X. Zheng, and A. Cichocki, "Matrix-group algorithm via improved whitening process for extracting statistically independent sources from array signals," IEEE Trans on Signal Processing, 2007, 55 (3), pp: 962-977.
[72] A. Souloumiac, "Nonorthogonal joint diagonalization by combining Givens and Hyperbolic rotation," IEEE Trans on Signal Processing, 2009, 57 (6), pp: 2222-2231.
[73] R. Vollgraf and K. Obermayer, "Quadratic optimization for simultaneous matrix diagonalization," IEEE Trans on Signal Processing, 2006, 54 (9), pp: 3270-3278.
[74] B. Afsari, "Simple LU and QR based non-orthogonal matrix joint diagonalizaiton " Proc. 6th Int Conf ICA & BSS, Berlin Germany, 2006, pp: 1-7.
[75] F. Wang, Z. Liu, and J. Zhang, "Nonorthogonal joint diagonalization algorithm based on trigonometric parametrization," IEEE Trans on Signal Processing, 2007, 55 (11), pp: 5299-5308.
[76] N. D. Sidiropoulos, R. Bro, and G. B. Giannakis, "Parallel factor analysis in sensor array processing," IEEE Trans on Signal Processing, 2000, 48 (8), pp: 2377-2388.
[77] A. Van der Veen, "Joint diagonalization via subspace fitting techniques," 2001, pp: 2773-2776.
[78] A. Yeredor, "Non-orthogonal joint diagonalization in the least-squares sense with application in blind source separation," IEEE Trans on Signal Processing, 2002, 50 (7), pp: 1545-1553.
[79] D. Z. Feng, X. D. Zhang, and Z. Bao, "An efficient multistage decomposition approach for independent components," Signal processing, 2003, 83 (1), pp: 181-197.
[80] Y. Q. Li and J. Wang, "Sequential blind extraction of instantaneously mixed sources," IEEE Trans on Signal Processing, 2002, 50 (5), pp: 997-1006.
[81]凌燮亭, "近场宽带信号源的盲分离,"电子学报, 1996, 24 (7), pp: 87-92.
[82]张昕,胡波,凌燮亭, "盲信号分离在数字无线通信中的一种应用,"通信学报, 2000, 21 (2), pp: 73-77.
[83] Z. Y. He, J. Liu, and L. X. Yang, "Blind separation of mages using edge worth expansion based ICA algorithm," Chinese Journal of Electronics, 1999, 8 (3), pp: 278-282.
[84] Z. Y. He, L. X. Yang, and J. Liu, "Blind source separation using cluster based multivariate density estimation algorithm," IEEE Trans on Signal Processing, 2000, 48 (2), pp: 575-579.
[85]刘琚,聂开宝,何振亚, "非线性混叠信号的可分离性及分离方法研究,"电子与信息学报, 2003, 25 (1), pp: 54-61.
[86]刘琚,孙建德., "基于ICA的数字水印方法,"电子学报, 2004, 32 (4), pp: 657-660.
[87]虞晓,胡光锐, "基于统计估计的盲信号分离算法,"上海交通大学学报, 1999, 33 (5), pp: 566-569.
[88]倪晋平,马远良,孙超,童.立, "用独立成份分析算法实现水声信号盲分离,"声学学报, 2002, 27 (4), pp: 321-326.
[89]倪晋平,马远良,鄢社锋, "基于高阶累积量的复数混合矩阵盲估计算法,"电子与信息学报, 2002, 24 (11), pp: 1506-1511.
[90]倪晋平,马远良,鄢社锋,杨坤德, "水声信号盲分离算法性能评价准则,"探测与控制学报, 2002, 24 (2), pp: 40-44.
[91]倪晋平,马远良,杨坤德, "水声信号盲分离的实数算法与复数算法,"西北工业大学学报, 2002, 20 (1), pp: 44-48.
[92]谢胜利,王杰, "一个基于盲信号分离的多路回波消除结构,"电子学报, 2004, 32 (7), pp: 1124-1126.
[93]章晋龙,何昭水,谢胜利,刘海林, "多个源信号混叠的盲分离几何算法,"计算机学报, 2005, 28 (9), pp: 1575-1580.
[94]何昭水,谢胜利,傅予力, "信号的稀疏性分析,"自然科学进展, 2006, 16 (9), pp: 1167-1173.
[95]张安清,邱天爽,章新华, "分数低阶矩的信号盲分离方法,"通信学报, 2006, 27 (3), pp: 32-36.
[96] T. P. Chen and Q. Lin, "Dynamic behavior of the whitening process signal," IEEE Signal Processing Letters, 1998, 5 (1), pp: 25-26.
[97] Y. Tan, J. Wang, and J. M. Zurada, "Nonlinear blind source separation using a radial basis function network," IEEE Trans on Neural Networks, 2001, 12 (1), pp: 124-134.
[98] L. Q. Zhang, A. Cichocki, and S. Amari, "Self-adaptive blind source separation based on activation functions adaptation," IEEE Transactions on Neural Networks, 2004, 15 (2), pp: 233– 244.
[99] X. L. Zhu and X. D. Zhang, "Adaptive RLS algorithm for blind source separation using a natural gradient," IEEE Signal Processing Letters, 2002, 9 (12), pp: 432-435.
[100] X. L. Zhu, X. D. Zhang, Z. Ding, and Y. Jia, "Adaptive nonlinear PCA algorithms for blind sourc separation without prewhitening," IEEE TRans on Circuits and Systems-I, 2006, 53 (3), pp: 745-753.
[101] J. M. Ye, X. L. Zhu, and X. D. Zhang, "Adaptive blind separation with an unknown number of sources," Neural Computaiton, 2004, 16 (8), pp: 1641-1660.
[102] Q. Lv and X. D. Zhang, "A unified method for blind separation of sparse source with unknown source number," IEEE Signal Processing Letters, 2006, 13 (1), pp: 49-51.
[103]张贤达,保铮, "盲信号分离,"电子学报, 2001, 29 (12A), pp: 1766-1771.
[104]朱孝龙,保铮,张贤达, "基于分阶段学习的盲信号分离,"中国科学E辑, 2002, 32 (5), pp: 693-703.
[105]朱孝龙,张贤达,冶继民, "基于自然梯度的递归最小二乘盲信号分离,"中国科学E辑, 2003, 33 (8), pp: 741-748.
[106] S. T. Lou and X. D. Zhang, "Fuzzy-based learning rate determination for blind source separation," IEEE Trans on Fuzzy Systems, 2003, 11 (3), pp: 375-383.
[107] B. Widrow, J. R. Glover, J. M. McCool, and J. Kaunite, "Adaptive noise cancelling:principle and application," IEEE Proceedings, 1975, 63), pp: 1692-1716.
[108] R. L. Zinser, G. Mirchandani, and J. B. Evans, "Some experimental and theoretical results using a new adaptive filter structure for noise cancellation in the presence of crosstalk," Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'85), 1985, pp: 1253-1256.
[109] D. Van-Compernolle and S. Van-Gerven, "Signal separation in a symmetric adaptive noise cannceller by output decorrelation," IEEE Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP'92), 1992, IV, pp: 221-224.
[110] S. Van-Gerven and D. Van-Compernolle, "Sigal separation by symmetric adaptive decorrelation: stability, convergence and uniqueness," IEEE Trans on Signal Processing, 1995, 43 (7), pp: 1602-1612.
[111] X. Sun and S. C. Douglas, "Multichannel blind deconvolution of arbitrary signals: adaptive algorithms and stabiltiy analyisis," Proceedings of 34th Asilomar Conference on Signals System Computer, 2000, 2), pp: 1412-1416.
[112] S. Choi, H. Hong, H. Glotin, and F. Berthommier, "Multichannel signal separation for cocktail party speech recognition: a dynamic recurrent network," Neurocomputing, 2002, 49, pp: 299-314.
[113] S. C. Douglas, H. Sawada, and S. Makino, "Natural gradient multichannel blind deconvolution and speech separation using causal FIR filters," IEEE Trans on Speech and Audio Processing, 2005, 13 (1), pp: 92-104.
[114] E. Weinstein, M. Feder, and A. V. Oppenheim, "Multi-channel signal separation by decorrelation," IEEE Trans on Speech and Audio Processing, 1993, 1 (4), pp: 405-413.
[115] V. S. Gerven and V. D. Compernolle, "Signal separation by symmetric adaptive decorrelation stability, convergence and uniqueness," IEEE Trans on Signal Processing, 1995, 43 (7), pp: 1602-1612.
[116] M. Kawamoto, K. Matsuoka, and N. Ohnishi, "A method of blind separation for convolved non-stationary signals," Neurocomputing, 1998, 22, pp: 157-171.
[117] H. Bousbia-Salah and A. Belouchrani, "Jacobi-like algorithm for blind signal separation of convolutive mixtures," Electronics Letters, 2001, 36 (16), pp: 1049-1050.
[118] H. Bousbia-Salah, A. Belouchrani, and K. Abed-Meraim, "Blind separation of convolutive mixtures using joint block diagonalization," IEEE proceedings of International Symposium on Signal Processing and its Applications (ISSPA'01), 2001, 1, pp: 13-16.
[119] K. Abed-Meraim and A. Belouchrani, "Algorithms for joint block diagonalization,"Proc. 12th European Signal Processing Conference (EUSIPCO'04), Vienna, Austria, 2004, pp: 209-212.
[120] F. J. Theis, "Blind signal separation into group of dependent signals using joint block diagonalization," Proc. International Symposium on Circuits and Systems, Kobe, Japan, 2005, pp: 5878-5881.
[121] C. Fevotte and F. J. Theis, "Orthogonal approximate joint block-diagonalization," Technical Report GET/Télécom, Pairs 2007.
[122] H. Ghennioui, E. M. Fadaili, and N. T. Moreau, "A nonunitary joint block diagonalization algorithm for blind separation of convolutive mixtures of sources," IEEE Signal Processing Letters, 2007, 14 (11), pp: 860–863.
[123] E. M. Fadaili, T. N. Moreau, and E. Moreau, "Non-orthogonal joint diagonalization/zero-diagonalization for source separation based on time-frequency distributions," IEEE Trans on Signal Processing, 2007, 55 (5), pp: 1673-1687.
[124] P. Smaragdis, "Blind separation of convolved mixtures in the frequency domain," Neurocomputing, 1998, 22, pp: 21-34.
[125] S. Araki, S. Makino, T. Nishikawa, and H. Saruwatari, "Fundamental limitation of frequency domain blind source separation for convolutive mixture of speech," IEEE Trans on Speech and Audio Processing, 2003, 11 (2), pp: 109-116.
[126] L. Parra and C. Spence, "Convolutive blind separation of nonstationary sources," IEEE Trans. on Speech Audio Processing 2000, 8 (3), pp: 320-327.
[127] S. Kurita and H. Saruwatari, "Evaluation of blind signal separation method using directivity pattern under reverberant conditions," Proc. IEEE Acoustics, Speech, Signal Processing, 2000, pp: 3140-3143.
[128] M. Z. Ikram and D. R. Morgan, "A beamforming approach to permutation alignment for multichannel frequency-domain blind speech separation," 2002, pp: 881-884.
[129] H. Saruwatari, T. Kawamura, T. Nishikawa, A. Lee, and K. Shikano, "Blind source separation based on a fast-convergence algorithm combining ICA and beamforming," IEEE Trans on Speech and Audio Processing, 2006, 14 (2), pp: 666-678.
[130] Y. Zhou and B. Xu, "Blind source separation in frequency domain," Signal processing, 2000, 83, pp: 2037-2046.
[131] F. Asano, S. Ikeda, M. Ogawa, H. Asoh, and N. Kitawaki, "Combined approach of array processing and independent component analysis for blind separation of acoustic signals," IEEE Trans on Audio, Speech and Language Processing, 2003, 11 (3), pp: 204-215.
[132] N. Murata, S. Ikeda, and A. Ziehe, "An approach to blind source separation based ontemporal structure of speech signals," Neurocomputing, 2001, 41, pp: 1-24.
[133] J. Anemuller and B. Kollmeier, "Amplitude modulation decorrelation for convolutive blind source separation," Proceedings of the International Workshop on Independent Component Analysis and Blind Signal Separation, 2000, pp: 215-220.
[134] H. Sawada, R. Mukai, S. Araki, and S. Makino, "A robust approach to the permutation problem of frequency-domain blind source separation," IEEE Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP'03), 2003, V, pp: 381-384.
[135] H. Sawada, R. Mukai, and S. Araki, "A robust and precise method for solving the permutation problem of frequency-domain blind source separation," IEEE Trans on Speech and Audio Processing, 2004, 12 (5), pp: 530-538.
[136] L. Parra and C. Alvino, "Geometric source separation: Merging convolutive source separation with geometric beamforming," IEEE Trans on Speech and Audio Processing, 2002, 10 (6), pp: 352-362.
[137] M. Knaak, S. Araki, and S. Makino, "Geometrically Constrained Independent Component Analysis," IEEE Trans on Audio, Speech and Language Processing, 2007, 15 (2), pp: 715-726.
[138] Q. F. Pan and T. Aboulnasr, "A new perceputal convolutive blind source separation algorithm for speech separation," Proc. International Conference on Signal Processing, 2004, pp: 323-326.
[139] S. Y. Low, S. Nordholm, and R. Togneri, "Convolutive blind signal separation with post-processing," IEEE Trans on Speech and Audio Processing, 2004, 12 (5), pp: 539-548.
[140] Y. T. Huang, J. Benesty, and J. D. Chen, "A blind channel identification-based two-stage approach to separation and dereverberation of speech signals in a reverberant environment," IEEE Trans on Speech and Audio Processing, 2005, 13 (5), pp: 882-895.
[141] B. Rivet, L. Girin, and C. Jutten, "Mixing audiovisual speech processing and blind source separation for the extraction of speech signals from convolutive mixtures," IEEE Trans on Audio, Speech and Language Processing, 2007, 15 (1), pp: 96-108.
[142] O. Yilmaz and S. Rickard, "Blind separation of speech mixtures via time-frequency masking," IEEE Trans on Signal Processing, 2004, 52 (7), pp: 1830–1847.
[143] H. Sawada, S. Araki, R. Mukai, and S. Makino, "Blind extraction of dominant target sources using ICA and time-frequency masking," IEEE Trans on Audio, Speech and Language Processing, 2006, 14 (6), pp: 2165–2173.
[144] A. Aissa, K. Aed-Meraim, and Y. Grenier, "Blind separation of underdetermined convolutive mixtures using their time-frequency representation," IEEE Trans on Audio, Speech and Language Processing, 2007, 15 (5), pp: 1540-1550.
[145]何培宇,殷斌, P. C. W. Sommen, "一种有效的语音盲信号分离简化混合模型,"电子学报, 2002, 30 (10), pp: 1167-1173.
[146]何培宇,周激流,夏秀渝,王永德,赵刚, "一种基于二阶盲信号分离的多路声回波抑制模型,"电子学报, 2006, 34 (11), pp: 2110-2114.
[147]苏野平,何量,杨荣震,朱小刚, "一种改进的基于高阶累积量的语音盲分离算法,"电子学报, 2002, 30 (7), pp: 956-958.
[148] T. M. Mei, J. T. Xi, F. L. Yin, A. Mertins, and J. F. Chicharo, "Blind source separation based on time-domain optimization of a frequency-domain independence criterion," IEEE Trans on Audio, Speech and Language Processing, 2006, 14 (6), pp: 2075-2085.
[149] P. Comon and E. Moreau, "Blind MIMO equalizaiton and joint-diagonalization criteria," IEEE Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP'01), 2001, 5, pp: 2749-2752.
[150] P. Comon, E. Moreau, and L. Rota, "Blind separation of convolutive mixtures: a contrast-based joint diagonalization approach," Proceedings of the International Symposium on Independent Component Analysis and Blind Signal Separation (ICA'01), 2001, pp: 686-691.
[151] A. Ziehe, P. Laskov, G. Nolte, and K. R. Muller, "A fast algorithm for joint diagonalization with non-orthogonal transformations and its applicaiton to blind source separation," Journal of Machine Learning Research, 2004, 5, pp: 777-800.
[152] G. Chabriel, J. Barrere, T. N. Moreau, and E. Moreau, "Algebraic joint zero-diagonalization and blind sources separation," IEEE Trans on Signal Processing, 2008, 56 (3), pp: 980-989.
[153] L. Tong, Y. Inouye, and R. W. Liu, "Waveform-preserving blind estimation of multiple independent sources," IEEE Trans on Signal Processing, 1993, 41 (7), pp: 2461-2470.
[154] R. A. Horn and C. R. Johnson, Matrix Analysis. UK: Cambridge University Press 1985.
[155] M. Wax and T. Kailath, "Detection of signals by information theoretic contrast function," IEEE Trans on Audio, Speech and Language Processing, 1985, 33 (2), pp: 387-392.
[156] F. X. Wang, Z. K. Liu, and J. Zhang, "A new joint diagonalization algorithm with application in blind source separation," IEEE Signal Processing Letters, 2006, 13 (1), pp: 41-44.
[157] D.-T. Pham and J.-F. Cardoso, "Blind separation of instantaneous mixtures of nonstationary sources," IEEE Trans on Signal Processing, 2001, 49 (9), pp: 1837-1848.
[158] M. Wax and J. Sheinvald, "A least-squares approach to joint diagonalizaiton," IEEE Signal Processing Letters, 1997, 4 (2), pp: 52-53.
[159] X. L. LI and X. D. Zhang, "Nonorthogonal joint diagonaliation free of degenerate solution," IEEE Trans on Signal Processing, 2007, 55 (5), pp: 1803-1814.
[160] J. P. LaSalle, The Stability of Dynamical System. Philadelphia: PA: SIAM Press, 1976.
[161] A. Ciaramella and R. Tagliaferri, "Amplitude and permutation indeterminacies in frequency domain convolved ICA," IEEE Proceedings of International Conference on Neural Networks, 2003, 1, pp: 708-713.
[162] V. Capdevielle, C. Serviere, and J. L. Lacoume, "Blind separation of wide-band sources in the frequency domain," IEEE Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP'95), 1995, pp: 2080-2083.
[163] D. W. E. Schobben and P. Sommen, "A frequency domain blind signal separation method based on decorrelation," IEEE Trans on Signal Processing, 2002, 50 (8), pp: 1855-1865.
[164] H. Sawada, R. Mukai, S. Araki, and S. Makino, "Polar coordinate based nonlinear function for frequency-domain blind source separation," IEEE Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP'02), 2002, I, pp: 1001-1004.
[165] R. H. Lambert, "Multichannel blind deconvolution: FIR matrix algebra and separation of multipath mixtures," Univ. Southern California Los Angeles, Ph.D, 1996.
[166] I. Sabala, A. Cichocki, and S. Amari, "Relationships between instantaneous blind source separation and multichannel blind deconvolution," IEEE Neural Networks proceedings of World Congress on Computational Intelligence, 1998, 1, pp: 39-44.
[167] K. Rahbar and J. Reilly, "Blind source separation of convolved sources by joint approximate diagonalization of cross-spectral density matrices," IEEE Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP'01), 2001, 5, pp: 2745-2748.
[168]梅铁民, "盲源信号分离时域与频域算法研究,"大连理工大学,博士, 2005.
[169] T. M. Mei and F. L. Yin, "Decorrelation-based on blind source separation algorithm and convergence analysis," Proceedings of the 4th International Symposium on Independent Component Analysis and Blind Signal Separation (ICA2003), 2003, pp: 457-461.
[170] T. M. Mei and F. L. Yin, "Blind separation of convolutive mixtures by decorrelation," Signal processing, 2004, 84, pp: 2297-2313.
[171] T. M. Mei, A. Mertins, F. Yin, J. T. Xi, and J. F. Chicharo, "Blind source separation for convolutive mixtures based on the joint diagonalization of power spectral density matrices," Signal processing, 2008, 88, pp: 1990-2007.
[172] T. M. Mei and A. Mertins, "Convolutive blind source separation based on disjointness maximization of subband signals," IEEE Signal Processing Letters, 2008, 15, pp: 725-728.
[173] K. Matsuoka, "Independent component analysis and its application to sound signal separation," Proc. IWAEN2003, Kyoto, Japan, 2003, pp: 15-18.
[174] R. H. Lambert and A. J. Bell, "Blind separation of multiple speaker in a multipath environment," IEEE Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP'97), 1997, 1, pp: 423-426.
[2] S. Haykin, Unsupervised adaptive filtering, vol. 1. New York: Wiley, 2000.
[3] A. Hyvarinen, J. Karhunen, and E. Oja, Independent Conponent Anaylysis. New York: Wiley, 2001.
[4]张贤达,保铮,通信信号处理:国防工业出版社, 2002.
[5] P. Georgiev and A. CIchocki, "Robust independent component analysis via time-delay cumulant function," IEICE Trans. on Fundamentals, 2003, E86-A (3), pp: 573-580.
[6] J. V. Stone, Independent Component Analysis: MIT Press, 2004.
[7]杨福生,洪波,独立分量分析的原理与应用:清华大学出版社, 2006.
[8] S. Makino, T. W. Lee, and H. Sawada, Blind speech separation: Springer, 2007.
[9]史习智,盲信号处理-理论与实践:上海交通大学出版社, 2008.
[10] J. K. Tugnait, "Identification and deconvolution of multichannel linear non-Gaussian processes using higher-order statistices and inverse filter criteria," IEEE Trans on Signal Processing, 1997, 45 (3), pp: 658-672.
[11] S. C. Douglas, "Fixed-point algorithms for the blind separation of arbitrary complex-valued non-Gaussians signal mixtures," EURASIP Journal on advances in Signal Processing, 2007, 1 (3), pp: 1-15.
[12] J. F. Cardoso and A. Souloumiac, "Blind beamforming for non-Gaussian signals," IEE Proceedings-F, 1993, 140 (6), pp: 362-370.
[13] A. Souloumiac, "Blind source detection and separation using second order non-stationarity," Proc. IEEE Acoustics, Speech and Signal Processing, 1995, pp: 1912-1915.
[14] A. Hyvarinen, "Blind source separation by nonstationarity of variance: a cumulant-based approach," IEEE Trans on Neural Networks, 2001, 12 (6), pp: 1471-1474.
[15] F. L. Yin, T. M. Mei, and J. Wang, "Blind source separation based on decorrelation and nonstationarity," IEEE trans on Circuits and Systems-I: Regular Papers, 2007, 54 (5), pp: 1150-1158.
[16] L. Molgedey and H. G. Schuster, "Separation of a mixture of independent signals using time delayed correlations," Phyical Review Letters, 1994, 72 (23), pp: 3634-3637.
[17] S. Amari and A. Cichocki, "Adaptive blind signal processing-neural network aprroachs," Proceedings of the IEEE, 1998, 86 (10), pp: 2026-2048.
[18] J.-F. Cardoso, "Blind signal separation: statistical principle " Proceedings of the IEEE, 1998, 86 (10), pp: 2009-2025.
[19] H. H. Yang, "Serial updating rule for blind separation derived from the method of scoring," IEEE Signal Processing Letters, 1999, 47 (8), pp: 2275-2285.
[20] A. J. Bell and T. J. Sejnowski, "An information maximization approach to blind separation and blind deconvolution," Neural Computaiton, 1995, 7 (6), pp: 1129-1159.
[21] S. Amari, A. Cichocki, and H. Yang, "A new learning algorithm for blind signal separation.Advances in Neural Information Processing Systems," MA, 1996.
[22] J. F. Cardoso, "Infomax and maximum likelihood for blind source separation," IEEE Signal Processing Letters, 1997, 4, pp: 112-114.
[23] J. Karhunen and J. Joutsensal, "Representation and separation of signals using nonlinear PCA type learing," Neural Networks, 1994, 7 (1), pp: 113-127.
[24] C. L. Nikias and A. P. Petropulu, Higher-order spectra analysis: a nonlinear signal processing framwork. Nnglewood Cliffs: Prentice-Hall, 1993.
[25] Y. Tan and J. Wang, "Nonlinear blind source separation using higher order statistics and a genetic algorithm," IEEE Trans on Evolutionary Computation, 2001, 5 (6), pp: 600-612.
[26] J.-F. Cardoso, "Super-symmetric decompositon of the forth-order cumulant tensor, blind identification of more sources than sensors," Proc. IEEE Proceedings of international conference on Acoustic, Speech and Signal Processing (ICASSP'91), 1991, pp: 3109-3112.
[27] J.-F. Cardoso, "Source separation usign higher order moment," IEEE Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP'89), 1989, (4), pp: 2109-2112.
[28] C. Simon, P. Loubaton, C. Vignat, C. Jutten, and G. d'Urso, "Blind source separation of convolutive mixtures by maximization of fourth-order cumulants: the non i.d.d.case," Proceedings of the 32th Asilomar Conference on Signals, Systems&Computers, 1998, (2), pp: 1584-1588.
[29] L. De-Lathauwer, B. De-Moor, and J. Vandewalle, "Independent components analysis and (simultaneous) third-order tensor diagonalization," IEEE Trans on Signal Processing, 2001, 49 (10), pp: 2262-2271.
[30] X. L. LI and X. D. Zhang, "Sequential blind extraction adopting Second-order statistics," IEEE Signal Processing Letters, 2006, 14 (1), pp: 58-61.
[31] J.-F. Cardoso and B. H. Laheld, "Equivariant adaptive source separation," IEEE Trans on Signal Processing, 1996, 44 (12), pp: 3017-3030.
[32] A. Hyvarinen, "Fast and robust fixed-point algorithms for independent component analysis," IEEE Trans on Neural Networks, 1999, 10 (3), pp: 626-634.
[33] V. Zarzoso and A. K. Nandi, "Blind separation of independent sources for virtually any source probability density function " IEEE Trans on Signal Processing, 1999, 49 (9), pp: 2419-2432.
[34] V. Zarzoso and A. K. Nandi, "Adaptive blind source separation for virtually any source probability density function " IEEE Trans on Signal Processing, 2000, 48 (2), pp: 477-489.
[35] D. Yellin and E. Weinstein, "Criteria for multichannel signal separation," IEEE Trans on Signal Processing, 1994, 42 (8), pp: 2156-2168.
[36] D. Yellin and E. Weinstein, "Multichannel signal separation: methods and analysis," IEEE Trans on Signal Processing, 1996, 44 (1), pp: 106-118.
[37] P. Comon, "Independent component analysis: A new concept?" Signal Processing, 1994, 36 (3), pp: 287-314.
[38] A. Hyvarinen and E. Oja, "A fast fixed-point algorithm for independent component analysis," Neural Computaiton, 1997, 9, pp: 1483-1492.
[39] Z. Ding and T. Nguyen, "Stationary points of a kurtosis maximization algorithm for blind signal separation and antenna beamforming," IEEE Trans on Signal Processing, 2000, 48 (6), pp: 1587-1596.
[40] S. Choi, A. Cichocki, and A. Belouchrani, "Blind separation of second-order nonstationary and temporally colored sources," Proceedings of the 11th IEEE signal processing workshop, 2001, pp: 444-447.
[41] M. Kawamoto and Y. Inouye, "Blind deconvolution of MIMO-FIR systems with colored inputs using second order statistices," IEICE Trans. on Fundamentals, 2003, E86-A (3), pp: 597-604.
[42] A. Belouchrani, K. Abed-Meraim, J.-F. Cardoso, and E. Moulines, "A blind source separation technique using second-order statistics," IEEE Trans on Signal Processing, 1997, 45 (2), pp: 433-443.
[43] R. Zhang and M. K. Tsatsanis, "A second-order method for blind separaiton of non-stationary sources," IEEE Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP'01), 2001, (5), pp: 2729-2800.
[44] N. Delfosse and P. Loubaton, "Adaptive blind separation of independent sources : A second-order stable algorithm for the general case," IEEE Trans On Circuits and Systems-I: Fundamental Theory and Applications, 2000, 47 (7), pp: 1057-1071.
[45] U. A. Lindgren and H. Broman, "Source separation using a criterion beas onsecond-order statistices," IEEE Trans on Signal Processing, 1998, 46 (7), pp: 1837-1850.
[46] Y. Xiang, K. Abed-Meraim, and Y. Hua, "Adaptive blind source separatio by second order statistics and natural gradient," IEEE Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP'99), 1999, pp: 2917-2920.
[47] H. Buchner, R. Aichner, and W. Kellermann, "A generalization of blind source separation algorithm for convolutive mixtures based on second-order statistics," IEEE Trans on Speech and Audio Processing, 2005, 13 (1), pp: 120-134.
[48] L. Tong, R. Liu, V. C. Soon, and Y.-F. Huang, "Indeterminacy and identifiability of blind identification," IEEE Trans on Circuits and Systems, 1991, 38 (5), pp: 499-509.
[49] L. Tong and R. Liu, "Blind estimation of correlated source signals," Proceedings of ACSSC, 1990, (1), pp: 258-262.
[50] C. Chang, Z. Ding, S. F. Yau, and D. H. Y. Chan, "A matrix-pencil approach to blind separation of colored non-stationary signals," IEEE Signal Processing Letters, 2000, 7 (12), pp: 348-350.
[51] K. Matsuoka, M. Ohya, and M. Kawamoto, "A neural net for blind separation of nonstationary signals," Neural Computaiton, 1995, 8 (3), pp: 411-419.
[52] S. Choi, A. Cichocki, and S. Amari, "Equivariant nonstationary source separation," Neural Computaiton, 2002, 15, pp: 121-130.
[53] J. Herault and C. Jutten, "Space or time adaptive signal processing by neural network models," Proc. Neural Network for Computing: AIP Conference Proceeding, New York, 1986.
[54] C. Jutten and J. Herault, "Blind separation of soueces, Part I: an adaptive algorithm based on neuromimetic architecture," Signal processing, 1991, 24, pp: 1-10.
[55] P. Commo, C. Jutten, and J. Herault, "Blind separation of sources, Part II: Problems statement," Signal processing, 1991, 24, pp: 1-10.
[56] G. B. Giannakis and A. Swami, "New results on state-space and input-outpu identification of non-Gaussian processing using cumulants," Proc. SPIE'87, San Diego, C A, 1987, pp: 199-205.
[57] E. Sorouchyari, "Blind source separation, Part II: stability analysis," Signal processing, 1991, 24, pp: 21-29.
[58] A. Cichocki, R. Unbehauen, L. Moszczynski, and E. Rummaert, "A new on-line adaptive learning algorithm for blind separation of source signals," Proc. International Symposium on Artificial Nerual Network (ISANN'94), Taiwan, 1994, pp: 406-411.
[59] E. Oja, J. Karhunen, L. Wang, and R. Vigario, "Principle and independent componentsin neural networks-recent developments," Proc. The 7th Italian Workshop Neural Networks (WIRN'95), Vietri, Italy, 1995, pp: 20-26.
[60] N. Delfosse and P. Loubaton, "Adaptive separation of independent sources: a deflation approach," IEEE Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP'94), 1994, IV, pp: 41-44.
[61] N. Delfosse and P. Loubaton, "Adaptive blind separatio of independent sources: a deflation approach," Signal processing, 1995, 45 (7), pp: 59-83.
[62] A. Cichocki and R. Unbehauen, "Robust neural networks with on-line learning for blind identification and blind separation," IEEE Trans on Circuits and Systems-I, 1996, 43 (11), pp: 894-906.
[63] A. Amari, "Natural gradient works efficiently in learning," Neural Computaiton, 1998, (10), pp: 251-276.
[64] A. Cichocki and A. Amari, Adaptive blind signal and image processing. New York: Wiley, 2003.
[65] H. H. Yang and A. Amari, "Adaptive on-line learning algorithms for blind separation maximum entropy and minimum mutual infromation," Neural Computaiton, 1997, 9 (7), pp: 1457-1482.
[66] X. R. Cao and R. W. Liu, "General approach to blind source separation," IEEE Trans on Signal Processing, 1996, 44 (3), pp: 562-571.
[67] A. Amari and J. F. Cardoso, "Blind source separation: semi-parametric statistical approach," IEEE Trans on Signal Processing, 1997, 45 (11), pp: 2692-2700.
[68] J. F. Cardoso, "On the stability of source separation algorithms," Jouranl of VLSI Signal Processing, 2000, 26 (1/2), pp: 7-23.
[69] S. Amari, S. C. Douglas, A. Cichocki, and H. H. Yang, "Multichannel blind deconvolution and equalization using the natural gradient," IEEE Proceedings of International Workshop Wireless Communication, 1997, pp: 101-104.
[70] A. Belouchrani and A. Cichocki, "A robust whitening procedure in blind source separation context," Electronics Letters, 2000, 36 (24), pp: 2050-2053.
[71] D. Z. Feng, W. X. Zheng, and A. Cichocki, "Matrix-group algorithm via improved whitening process for extracting statistically independent sources from array signals," IEEE Trans on Signal Processing, 2007, 55 (3), pp: 962-977.
[72] A. Souloumiac, "Nonorthogonal joint diagonalization by combining Givens and Hyperbolic rotation," IEEE Trans on Signal Processing, 2009, 57 (6), pp: 2222-2231.
[73] R. Vollgraf and K. Obermayer, "Quadratic optimization for simultaneous matrix diagonalization," IEEE Trans on Signal Processing, 2006, 54 (9), pp: 3270-3278.
[74] B. Afsari, "Simple LU and QR based non-orthogonal matrix joint diagonalizaiton " Proc. 6th Int Conf ICA & BSS, Berlin Germany, 2006, pp: 1-7.
[75] F. Wang, Z. Liu, and J. Zhang, "Nonorthogonal joint diagonalization algorithm based on trigonometric parametrization," IEEE Trans on Signal Processing, 2007, 55 (11), pp: 5299-5308.
[76] N. D. Sidiropoulos, R. Bro, and G. B. Giannakis, "Parallel factor analysis in sensor array processing," IEEE Trans on Signal Processing, 2000, 48 (8), pp: 2377-2388.
[77] A. Van der Veen, "Joint diagonalization via subspace fitting techniques," 2001, pp: 2773-2776.
[78] A. Yeredor, "Non-orthogonal joint diagonalization in the least-squares sense with application in blind source separation," IEEE Trans on Signal Processing, 2002, 50 (7), pp: 1545-1553.
[79] D. Z. Feng, X. D. Zhang, and Z. Bao, "An efficient multistage decomposition approach for independent components," Signal processing, 2003, 83 (1), pp: 181-197.
[80] Y. Q. Li and J. Wang, "Sequential blind extraction of instantaneously mixed sources," IEEE Trans on Signal Processing, 2002, 50 (5), pp: 997-1006.
[81]凌燮亭, "近场宽带信号源的盲分离,"电子学报, 1996, 24 (7), pp: 87-92.
[82]张昕,胡波,凌燮亭, "盲信号分离在数字无线通信中的一种应用,"通信学报, 2000, 21 (2), pp: 73-77.
[83] Z. Y. He, J. Liu, and L. X. Yang, "Blind separation of mages using edge worth expansion based ICA algorithm," Chinese Journal of Electronics, 1999, 8 (3), pp: 278-282.
[84] Z. Y. He, L. X. Yang, and J. Liu, "Blind source separation using cluster based multivariate density estimation algorithm," IEEE Trans on Signal Processing, 2000, 48 (2), pp: 575-579.
[85]刘琚,聂开宝,何振亚, "非线性混叠信号的可分离性及分离方法研究,"电子与信息学报, 2003, 25 (1), pp: 54-61.
[86]刘琚,孙建德., "基于ICA的数字水印方法,"电子学报, 2004, 32 (4), pp: 657-660.
[87]虞晓,胡光锐, "基于统计估计的盲信号分离算法,"上海交通大学学报, 1999, 33 (5), pp: 566-569.
[88]倪晋平,马远良,孙超,童.立, "用独立成份分析算法实现水声信号盲分离,"声学学报, 2002, 27 (4), pp: 321-326.
[89]倪晋平,马远良,鄢社锋, "基于高阶累积量的复数混合矩阵盲估计算法,"电子与信息学报, 2002, 24 (11), pp: 1506-1511.
[90]倪晋平,马远良,鄢社锋,杨坤德, "水声信号盲分离算法性能评价准则,"探测与控制学报, 2002, 24 (2), pp: 40-44.
[91]倪晋平,马远良,杨坤德, "水声信号盲分离的实数算法与复数算法,"西北工业大学学报, 2002, 20 (1), pp: 44-48.
[92]谢胜利,王杰, "一个基于盲信号分离的多路回波消除结构,"电子学报, 2004, 32 (7), pp: 1124-1126.
[93]章晋龙,何昭水,谢胜利,刘海林, "多个源信号混叠的盲分离几何算法,"计算机学报, 2005, 28 (9), pp: 1575-1580.
[94]何昭水,谢胜利,傅予力, "信号的稀疏性分析,"自然科学进展, 2006, 16 (9), pp: 1167-1173.
[95]张安清,邱天爽,章新华, "分数低阶矩的信号盲分离方法,"通信学报, 2006, 27 (3), pp: 32-36.
[96] T. P. Chen and Q. Lin, "Dynamic behavior of the whitening process signal," IEEE Signal Processing Letters, 1998, 5 (1), pp: 25-26.
[97] Y. Tan, J. Wang, and J. M. Zurada, "Nonlinear blind source separation using a radial basis function network," IEEE Trans on Neural Networks, 2001, 12 (1), pp: 124-134.
[98] L. Q. Zhang, A. Cichocki, and S. Amari, "Self-adaptive blind source separation based on activation functions adaptation," IEEE Transactions on Neural Networks, 2004, 15 (2), pp: 233– 244.
[99] X. L. Zhu and X. D. Zhang, "Adaptive RLS algorithm for blind source separation using a natural gradient," IEEE Signal Processing Letters, 2002, 9 (12), pp: 432-435.
[100] X. L. Zhu, X. D. Zhang, Z. Ding, and Y. Jia, "Adaptive nonlinear PCA algorithms for blind sourc separation without prewhitening," IEEE TRans on Circuits and Systems-I, 2006, 53 (3), pp: 745-753.
[101] J. M. Ye, X. L. Zhu, and X. D. Zhang, "Adaptive blind separation with an unknown number of sources," Neural Computaiton, 2004, 16 (8), pp: 1641-1660.
[102] Q. Lv and X. D. Zhang, "A unified method for blind separation of sparse source with unknown source number," IEEE Signal Processing Letters, 2006, 13 (1), pp: 49-51.
[103]张贤达,保铮, "盲信号分离,"电子学报, 2001, 29 (12A), pp: 1766-1771.
[104]朱孝龙,保铮,张贤达, "基于分阶段学习的盲信号分离,"中国科学E辑, 2002, 32 (5), pp: 693-703.
[105]朱孝龙,张贤达,冶继民, "基于自然梯度的递归最小二乘盲信号分离,"中国科学E辑, 2003, 33 (8), pp: 741-748.
[106] S. T. Lou and X. D. Zhang, "Fuzzy-based learning rate determination for blind source separation," IEEE Trans on Fuzzy Systems, 2003, 11 (3), pp: 375-383.
[107] B. Widrow, J. R. Glover, J. M. McCool, and J. Kaunite, "Adaptive noise cancelling:principle and application," IEEE Proceedings, 1975, 63), pp: 1692-1716.
[108] R. L. Zinser, G. Mirchandani, and J. B. Evans, "Some experimental and theoretical results using a new adaptive filter structure for noise cancellation in the presence of crosstalk," Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'85), 1985, pp: 1253-1256.
[109] D. Van-Compernolle and S. Van-Gerven, "Signal separation in a symmetric adaptive noise cannceller by output decorrelation," IEEE Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP'92), 1992, IV, pp: 221-224.
[110] S. Van-Gerven and D. Van-Compernolle, "Sigal separation by symmetric adaptive decorrelation: stability, convergence and uniqueness," IEEE Trans on Signal Processing, 1995, 43 (7), pp: 1602-1612.
[111] X. Sun and S. C. Douglas, "Multichannel blind deconvolution of arbitrary signals: adaptive algorithms and stabiltiy analyisis," Proceedings of 34th Asilomar Conference on Signals System Computer, 2000, 2), pp: 1412-1416.
[112] S. Choi, H. Hong, H. Glotin, and F. Berthommier, "Multichannel signal separation for cocktail party speech recognition: a dynamic recurrent network," Neurocomputing, 2002, 49, pp: 299-314.
[113] S. C. Douglas, H. Sawada, and S. Makino, "Natural gradient multichannel blind deconvolution and speech separation using causal FIR filters," IEEE Trans on Speech and Audio Processing, 2005, 13 (1), pp: 92-104.
[114] E. Weinstein, M. Feder, and A. V. Oppenheim, "Multi-channel signal separation by decorrelation," IEEE Trans on Speech and Audio Processing, 1993, 1 (4), pp: 405-413.
[115] V. S. Gerven and V. D. Compernolle, "Signal separation by symmetric adaptive decorrelation stability, convergence and uniqueness," IEEE Trans on Signal Processing, 1995, 43 (7), pp: 1602-1612.
[116] M. Kawamoto, K. Matsuoka, and N. Ohnishi, "A method of blind separation for convolved non-stationary signals," Neurocomputing, 1998, 22, pp: 157-171.
[117] H. Bousbia-Salah and A. Belouchrani, "Jacobi-like algorithm for blind signal separation of convolutive mixtures," Electronics Letters, 2001, 36 (16), pp: 1049-1050.
[118] H. Bousbia-Salah, A. Belouchrani, and K. Abed-Meraim, "Blind separation of convolutive mixtures using joint block diagonalization," IEEE proceedings of International Symposium on Signal Processing and its Applications (ISSPA'01), 2001, 1, pp: 13-16.
[119] K. Abed-Meraim and A. Belouchrani, "Algorithms for joint block diagonalization,"Proc. 12th European Signal Processing Conference (EUSIPCO'04), Vienna, Austria, 2004, pp: 209-212.
[120] F. J. Theis, "Blind signal separation into group of dependent signals using joint block diagonalization," Proc. International Symposium on Circuits and Systems, Kobe, Japan, 2005, pp: 5878-5881.
[121] C. Fevotte and F. J. Theis, "Orthogonal approximate joint block-diagonalization," Technical Report GET/Télécom, Pairs 2007.
[122] H. Ghennioui, E. M. Fadaili, and N. T. Moreau, "A nonunitary joint block diagonalization algorithm for blind separation of convolutive mixtures of sources," IEEE Signal Processing Letters, 2007, 14 (11), pp: 860–863.
[123] E. M. Fadaili, T. N. Moreau, and E. Moreau, "Non-orthogonal joint diagonalization/zero-diagonalization for source separation based on time-frequency distributions," IEEE Trans on Signal Processing, 2007, 55 (5), pp: 1673-1687.
[124] P. Smaragdis, "Blind separation of convolved mixtures in the frequency domain," Neurocomputing, 1998, 22, pp: 21-34.
[125] S. Araki, S. Makino, T. Nishikawa, and H. Saruwatari, "Fundamental limitation of frequency domain blind source separation for convolutive mixture of speech," IEEE Trans on Speech and Audio Processing, 2003, 11 (2), pp: 109-116.
[126] L. Parra and C. Spence, "Convolutive blind separation of nonstationary sources," IEEE Trans. on Speech Audio Processing 2000, 8 (3), pp: 320-327.
[127] S. Kurita and H. Saruwatari, "Evaluation of blind signal separation method using directivity pattern under reverberant conditions," Proc. IEEE Acoustics, Speech, Signal Processing, 2000, pp: 3140-3143.
[128] M. Z. Ikram and D. R. Morgan, "A beamforming approach to permutation alignment for multichannel frequency-domain blind speech separation," 2002, pp: 881-884.
[129] H. Saruwatari, T. Kawamura, T. Nishikawa, A. Lee, and K. Shikano, "Blind source separation based on a fast-convergence algorithm combining ICA and beamforming," IEEE Trans on Speech and Audio Processing, 2006, 14 (2), pp: 666-678.
[130] Y. Zhou and B. Xu, "Blind source separation in frequency domain," Signal processing, 2000, 83, pp: 2037-2046.
[131] F. Asano, S. Ikeda, M. Ogawa, H. Asoh, and N. Kitawaki, "Combined approach of array processing and independent component analysis for blind separation of acoustic signals," IEEE Trans on Audio, Speech and Language Processing, 2003, 11 (3), pp: 204-215.
[132] N. Murata, S. Ikeda, and A. Ziehe, "An approach to blind source separation based ontemporal structure of speech signals," Neurocomputing, 2001, 41, pp: 1-24.
[133] J. Anemuller and B. Kollmeier, "Amplitude modulation decorrelation for convolutive blind source separation," Proceedings of the International Workshop on Independent Component Analysis and Blind Signal Separation, 2000, pp: 215-220.
[134] H. Sawada, R. Mukai, S. Araki, and S. Makino, "A robust approach to the permutation problem of frequency-domain blind source separation," IEEE Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP'03), 2003, V, pp: 381-384.
[135] H. Sawada, R. Mukai, and S. Araki, "A robust and precise method for solving the permutation problem of frequency-domain blind source separation," IEEE Trans on Speech and Audio Processing, 2004, 12 (5), pp: 530-538.
[136] L. Parra and C. Alvino, "Geometric source separation: Merging convolutive source separation with geometric beamforming," IEEE Trans on Speech and Audio Processing, 2002, 10 (6), pp: 352-362.
[137] M. Knaak, S. Araki, and S. Makino, "Geometrically Constrained Independent Component Analysis," IEEE Trans on Audio, Speech and Language Processing, 2007, 15 (2), pp: 715-726.
[138] Q. F. Pan and T. Aboulnasr, "A new perceputal convolutive blind source separation algorithm for speech separation," Proc. International Conference on Signal Processing, 2004, pp: 323-326.
[139] S. Y. Low, S. Nordholm, and R. Togneri, "Convolutive blind signal separation with post-processing," IEEE Trans on Speech and Audio Processing, 2004, 12 (5), pp: 539-548.
[140] Y. T. Huang, J. Benesty, and J. D. Chen, "A blind channel identification-based two-stage approach to separation and dereverberation of speech signals in a reverberant environment," IEEE Trans on Speech and Audio Processing, 2005, 13 (5), pp: 882-895.
[141] B. Rivet, L. Girin, and C. Jutten, "Mixing audiovisual speech processing and blind source separation for the extraction of speech signals from convolutive mixtures," IEEE Trans on Audio, Speech and Language Processing, 2007, 15 (1), pp: 96-108.
[142] O. Yilmaz and S. Rickard, "Blind separation of speech mixtures via time-frequency masking," IEEE Trans on Signal Processing, 2004, 52 (7), pp: 1830–1847.
[143] H. Sawada, S. Araki, R. Mukai, and S. Makino, "Blind extraction of dominant target sources using ICA and time-frequency masking," IEEE Trans on Audio, Speech and Language Processing, 2006, 14 (6), pp: 2165–2173.
[144] A. Aissa, K. Aed-Meraim, and Y. Grenier, "Blind separation of underdetermined convolutive mixtures using their time-frequency representation," IEEE Trans on Audio, Speech and Language Processing, 2007, 15 (5), pp: 1540-1550.
[145]何培宇,殷斌, P. C. W. Sommen, "一种有效的语音盲信号分离简化混合模型,"电子学报, 2002, 30 (10), pp: 1167-1173.
[146]何培宇,周激流,夏秀渝,王永德,赵刚, "一种基于二阶盲信号分离的多路声回波抑制模型,"电子学报, 2006, 34 (11), pp: 2110-2114.
[147]苏野平,何量,杨荣震,朱小刚, "一种改进的基于高阶累积量的语音盲分离算法,"电子学报, 2002, 30 (7), pp: 956-958.
[148] T. M. Mei, J. T. Xi, F. L. Yin, A. Mertins, and J. F. Chicharo, "Blind source separation based on time-domain optimization of a frequency-domain independence criterion," IEEE Trans on Audio, Speech and Language Processing, 2006, 14 (6), pp: 2075-2085.
[149] P. Comon and E. Moreau, "Blind MIMO equalizaiton and joint-diagonalization criteria," IEEE Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP'01), 2001, 5, pp: 2749-2752.
[150] P. Comon, E. Moreau, and L. Rota, "Blind separation of convolutive mixtures: a contrast-based joint diagonalization approach," Proceedings of the International Symposium on Independent Component Analysis and Blind Signal Separation (ICA'01), 2001, pp: 686-691.
[151] A. Ziehe, P. Laskov, G. Nolte, and K. R. Muller, "A fast algorithm for joint diagonalization with non-orthogonal transformations and its applicaiton to blind source separation," Journal of Machine Learning Research, 2004, 5, pp: 777-800.
[152] G. Chabriel, J. Barrere, T. N. Moreau, and E. Moreau, "Algebraic joint zero-diagonalization and blind sources separation," IEEE Trans on Signal Processing, 2008, 56 (3), pp: 980-989.
[153] L. Tong, Y. Inouye, and R. W. Liu, "Waveform-preserving blind estimation of multiple independent sources," IEEE Trans on Signal Processing, 1993, 41 (7), pp: 2461-2470.
[154] R. A. Horn and C. R. Johnson, Matrix Analysis. UK: Cambridge University Press 1985.
[155] M. Wax and T. Kailath, "Detection of signals by information theoretic contrast function," IEEE Trans on Audio, Speech and Language Processing, 1985, 33 (2), pp: 387-392.
[156] F. X. Wang, Z. K. Liu, and J. Zhang, "A new joint diagonalization algorithm with application in blind source separation," IEEE Signal Processing Letters, 2006, 13 (1), pp: 41-44.
[157] D.-T. Pham and J.-F. Cardoso, "Blind separation of instantaneous mixtures of nonstationary sources," IEEE Trans on Signal Processing, 2001, 49 (9), pp: 1837-1848.
[158] M. Wax and J. Sheinvald, "A least-squares approach to joint diagonalizaiton," IEEE Signal Processing Letters, 1997, 4 (2), pp: 52-53.
[159] X. L. LI and X. D. Zhang, "Nonorthogonal joint diagonaliation free of degenerate solution," IEEE Trans on Signal Processing, 2007, 55 (5), pp: 1803-1814.
[160] J. P. LaSalle, The Stability of Dynamical System. Philadelphia: PA: SIAM Press, 1976.
[161] A. Ciaramella and R. Tagliaferri, "Amplitude and permutation indeterminacies in frequency domain convolved ICA," IEEE Proceedings of International Conference on Neural Networks, 2003, 1, pp: 708-713.
[162] V. Capdevielle, C. Serviere, and J. L. Lacoume, "Blind separation of wide-band sources in the frequency domain," IEEE Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP'95), 1995, pp: 2080-2083.
[163] D. W. E. Schobben and P. Sommen, "A frequency domain blind signal separation method based on decorrelation," IEEE Trans on Signal Processing, 2002, 50 (8), pp: 1855-1865.
[164] H. Sawada, R. Mukai, S. Araki, and S. Makino, "Polar coordinate based nonlinear function for frequency-domain blind source separation," IEEE Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP'02), 2002, I, pp: 1001-1004.
[165] R. H. Lambert, "Multichannel blind deconvolution: FIR matrix algebra and separation of multipath mixtures," Univ. Southern California Los Angeles, Ph.D, 1996.
[166] I. Sabala, A. Cichocki, and S. Amari, "Relationships between instantaneous blind source separation and multichannel blind deconvolution," IEEE Neural Networks proceedings of World Congress on Computational Intelligence, 1998, 1, pp: 39-44.
[167] K. Rahbar and J. Reilly, "Blind source separation of convolved sources by joint approximate diagonalization of cross-spectral density matrices," IEEE Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP'01), 2001, 5, pp: 2745-2748.
[168]梅铁民, "盲源信号分离时域与频域算法研究,"大连理工大学,博士, 2005.
[169] T. M. Mei and F. L. Yin, "Decorrelation-based on blind source separation algorithm and convergence analysis," Proceedings of the 4th International Symposium on Independent Component Analysis and Blind Signal Separation (ICA2003), 2003, pp: 457-461.
[170] T. M. Mei and F. L. Yin, "Blind separation of convolutive mixtures by decorrelation," Signal processing, 2004, 84, pp: 2297-2313.
[171] T. M. Mei, A. Mertins, F. Yin, J. T. Xi, and J. F. Chicharo, "Blind source separation for convolutive mixtures based on the joint diagonalization of power spectral density matrices," Signal processing, 2008, 88, pp: 1990-2007.
[172] T. M. Mei and A. Mertins, "Convolutive blind source separation based on disjointness maximization of subband signals," IEEE Signal Processing Letters, 2008, 15, pp: 725-728.
[173] K. Matsuoka, "Independent component analysis and its application to sound signal separation," Proc. IWAEN2003, Kyoto, Japan, 2003, pp: 15-18.
[174] R. H. Lambert and A. J. Bell, "Blind separation of multiple speaker in a multipath environment," IEEE Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP'97), 1997, 1, pp: 423-426.