In underdetermined blind source separation, more sources are to be estimated from less observed mixtures without knowing source signals and the mixing matrix. This paper presents a robust clustering algorithm for unde...In underdetermined blind source separation, more sources are to be estimated from less observed mixtures without knowing source signals and the mixing matrix. This paper presents a robust clustering algorithm for underdetermined blind separation of sparse sources with unknown number of sources in the presence of noise. It uses the robust competitive agglomeration （RCA） algorithm to estimate the source number and the mixing matrix, and the source signals then are recovered by using the interior point linear programming. Simulation results show good performance of the proposed algorithm for underdetermined blind sources separation （UBSS）.展开更多
Underdetermined blind signal separation （BSS） （with fewer observed mixtures than sources） is discussed. A novel searching-and-averaging method in time domain （SAMTD） is proposed. It can solve a kind of problems ...Underdetermined blind signal separation （BSS） （with fewer observed mixtures than sources） is discussed. A novel searching-and-averaging method in time domain （SAMTD） is proposed. It can solve a kind of problems that are very hard to solve by using sparse representation in frequency domain. Bypassing the disadvantages of traditional clustering （e.g., K-means or potential-function clustering）, the durative- sparsity of a speech signal in time domain is used. To recover the mixing matrix, our method deletes those samples, which are not in the same or inverse direction of the basis vectors. To recover the sources, an improved geometric approach to overcomplete ICA （Independent Component Analysis） is presented. Several speech signal experiments demonstrate the good performance of the proposed method.展开更多
Aiming at the statistical sparse decomposition principle(SSDP) method for underdetermined blind source signal recovery with problem of requiring the number of active signals equal to that of the observed signals, whic...Aiming at the statistical sparse decomposition principle(SSDP) method for underdetermined blind source signal recovery with problem of requiring the number of active signals equal to that of the observed signals, which leading to the application bound of SSDP is very finite, an improved SSDP(ISSDP) method is proposed. Based on the principle of recovering the source signals by minimizing the correlation coefficients within a fixed time interval, the selection method of mixing matrix’s column vectors used for signal recovery is modified, which enables the choose of mixing matrix’s column vectors according to the number of active source signals self-adaptively. By simulation experiments, the proposed method is validated. The proposed method is applicable to the case where the number of active signals is equal to or less than that of observed signals, which is a new way for underdetermined blind source signal recovery.展开更多
针对欠定盲分离中时变混合矩阵的估计问题,在稀疏域二维最小偏差角算法的基础上,提出了一种改进的欠定盲分离时变混合矩阵估计算法。该算法通过判断原始阵各列上是否都有观测点聚集和聚集在原始阵上的观测点以外的点的聚集方向,来检测...针对欠定盲分离中时变混合矩阵的估计问题,在稀疏域二维最小偏差角算法的基础上,提出了一种改进的欠定盲分离时变混合矩阵估计算法。该算法通过判断原始阵各列上是否都有观测点聚集和聚集在原始阵上的观测点以外的点的聚集方向,来检测变化时刻;并利用基于点密度大区域检测算法估计混合矩阵。改进算法对于混合矩阵发生某些列增加、消失和变化时均能检测出变化,并且在大幅提高变化时刻检测概率和混合矩阵估计精度的同时,降低了复杂度。实验仿真结果表明,在20 d B信噪比时,混合矩阵估计精度提高了60%以上。展开更多
基金the Research Foundation for Doctoral Programs of Higher Education of China (Grant No.20060280003)the Shanghai Leading Academic Discipline Project (Grant No.T0102)
文摘In underdetermined blind source separation, more sources are to be estimated from less observed mixtures without knowing source signals and the mixing matrix. This paper presents a robust clustering algorithm for underdetermined blind separation of sparse sources with unknown number of sources in the presence of noise. It uses the robust competitive agglomeration （RCA） algorithm to estimate the source number and the mixing matrix, and the source signals then are recovered by using the interior point linear programming. Simulation results show good performance of the proposed algorithm for underdetermined blind sources separation （UBSS）.
基金Supported by the National Natural Science Foundation of China (Grant Nos. U0635001, 60505005 and 60674033)the Natural Science Fund of Guangdong Province (Grant Nos. 04205783 and 05006508)the Specialized Prophasic Basic Research Projects of the Ministry of Science and Technology of China (Grant No. 2005CCA04100)
文摘Underdetermined blind signal separation （BSS） （with fewer observed mixtures than sources） is discussed. A novel searching-and-averaging method in time domain （SAMTD） is proposed. It can solve a kind of problems that are very hard to solve by using sparse representation in frequency domain. Bypassing the disadvantages of traditional clustering （e.g., K-means or potential-function clustering）, the durative- sparsity of a speech signal in time domain is used. To recover the mixing matrix, our method deletes those samples, which are not in the same or inverse direction of the basis vectors. To recover the sources, an improved geometric approach to overcomplete ICA （Independent Component Analysis） is presented. Several speech signal experiments demonstrate the good performance of the proposed method.
文摘Aiming at the statistical sparse decomposition principle(SSDP) method for underdetermined blind source signal recovery with problem of requiring the number of active signals equal to that of the observed signals, which leading to the application bound of SSDP is very finite, an improved SSDP(ISSDP) method is proposed. Based on the principle of recovering the source signals by minimizing the correlation coefficients within a fixed time interval, the selection method of mixing matrix’s column vectors used for signal recovery is modified, which enables the choose of mixing matrix’s column vectors according to the number of active source signals self-adaptively. By simulation experiments, the proposed method is validated. The proposed method is applicable to the case where the number of active signals is equal to or less than that of observed signals, which is a new way for underdetermined blind source signal recovery.
文摘针对欠定盲分离中时变混合矩阵的估计问题,在稀疏域二维最小偏差角算法的基础上,提出了一种改进的欠定盲分离时变混合矩阵估计算法。该算法通过判断原始阵各列上是否都有观测点聚集和聚集在原始阵上的观测点以外的点的聚集方向,来检测变化时刻;并利用基于点密度大区域检测算法估计混合矩阵。改进算法对于混合矩阵发生某些列增加、消失和变化时均能检测出变化,并且在大幅提高变化时刻检测概率和混合矩阵估计精度的同时,降低了复杂度。实验仿真结果表明,在20 d B信噪比时,混合矩阵估计精度提高了60%以上。
