基于最小二乘方的语音信号实时盲分离

研究语音信号盲分离的实时算法.盲信号分离技术在视频会议系统、语音信号预处理以及生物医学信号处理中都得到广泛应用.在本文中,利用最小二乘方原理并结合语音信号非平稳的特点,对混合语音信号进行实时分离.实验结果表明,基于最小二乘方的算法是非常有效的实时盲信号分离算法.     

基于遗传算法的盲源信号分离技术研究

提出一种采用遗传算法进行盲信号分离的新方法,为盲信号分离领域提供一种新的研究思路与方法。该方法基于迁移策略,应用交叉和变异方法,生成新一代的染色体,对由多个源信号混合而成的信号进行盲信号分离。实例表明了该方法的有效性。     

基于混合型神经网络的自适应盲信号分离算法

不同的神经网络模型及其算法对信号分离的效能会产生不同的影响,寻找高效简便的神经网络自适应盲信号分离算法是目前的研究方向.根据控制系统传输信号在线监测控制的要求,提出一种基于前馈与反馈混合型神经网络模型的自适应盲信号分离算法,并应用于传输信号的分离.计算机仿真实验的结果表明该算法的有效性和快速性.     

基于遗传神经网络优化的过程信号盲分离算法

研究一种较新的盲信号神经网络分离(BSS)方法,用于过程信号去噪.由于盲信号分离神经网络存在容易陷入局部极小点、收敛速度慢的缺点,研究采用遗传算法优化盲信号分离神经网络权值的初值,将遗传算法与神经网络(HJNN)结合形成GA-HJNN算法,可迅速得到最佳盲信号分离神经网络的权值矩阵,实现对过程信号的去噪,并通过实验对2种算法进行了比较.     

盲小波算法在遥感图像去噪中的应用

根据盲信号分离原理和小波分析,提出了一种遥感图像去噪的盲小波算法,首先将遥感图像的个信号进行同深度小波分解,得到不同信号相应深度的小波系数和尺度系数,然后将小波系数进行软阈值法处理,并进一步对不同信号的同深度的小波系数和尺度系数进行盲分离,并提取与源信号相关的信号,最后通过信号重构估计源信号。这种将小波分析和盲信号分离技术有机结合的方法能够有效的消除遥感图像的噪声。通过对实际遥感图像的处理,并与其他去噪技术相比较,利用盲小波算法得到的结果更为理想。     

一种时域盲信号分离系统的DSP实现

盲信号分离是当前信号处理研究的热点课题之一,在无线数据通信、医学、语音以及地震信号处理等领域有着广阔的应用前景.本文基于一种可用于真实环境中的盲信号分离算法,采用TI公司的浮点DSP芯片(TMS320C6701)EVM板实现了一个语音信号的盲信号分离系统.经现场验证,该系统不仅达到了实时处理的要求,而且对真实房间中的两路语音信号的盲分离得到了较好的效果.     

独立分量分析中NLPCA-RLS算法IP核的设计

为解决实时性盲信号分离的问题,基于独立分量分析的模型,设计出了NLPCA-RLS算法的IP核.利用Simulink 和DSP Builder对算法中用到的乘法器、查找表、状态机等进行建模,通过Quartus Ⅱ综合后在Altera FPGA器件中进行硬件仿真.仿真实验分别采用人工生成的周期信号和真实的语音信号进行验证.实验结果表明,该IP核能很好的完成瞬时混合模型中盲信号的分离,具有很强的实用性.     

基于盲信号分离的回波抵消技术

针对数字直放站回波抵消技术中自适应滤波法在多径回波信道条件下不能完全消除次径回波的问题,提出了基于盲信号分离的直放站回波抵消方法。首先对施主天线接收的混合信号进行相空间重构,使观测信号的数目大于等于独立信源的数目;然后利用独立分量分析法（ICA）对重构的信号进行盲信号分离;最后根据各分离信号和发送信号的相关情况判断有用信号,实现回波消除。对复杂多径回波信道条件下的多载波全球移动通信系统（GSM）信源进行回波抵消测试,分离得到的有用信号的相关系数可以达到0.9593。表明盲信号分离的方法可以实现复杂多径信道下的直放站回波抵消,有效解决了传统的自适应滤波法存在的问题。     

一种基于多层神经网络的盲信号分离算法

基于多层神经网络,提出一种盲信号分离算法.该算法不对信号的密度模型做任何假设,通过多层神经网络估计任意信号的概率密度函数,并由此估计信号的评价函数.同其他方法相比,该方法不仅具有更好的分离性能,而且收敛速度较快.该方法可直接应用于所有以非线性函数代替评价函数的盲信号分离算法.实验验证了方法的有效性.     

一种基于后非线性盲分离的图像解混技术研究

文中将一种后非线性盲分离算法应用于图像解混,该算法不需要额外的附加源信号信息,实现了非线性混合图像的全盲分离.首先,对后非线性混合模型进行微分变换,形成如同线性瞬时混合模型的形式,经论证源信号的微分形式仍保留了源信号的统计特性,达到简化的目的;其次,依据信号的相关特性来建立相应的目标函数及其递推方式,实现盲信号分离目的;最后,通过仿真试验来验证文中算法的有效性、可行性.实验证明,所采用的算法计算量小、收敛速度快、分离指标高,实现了混合图像的全盲分离,扩大了盲分离算法在图像解混技术中的应用范围及影响.     

A simplified approach to independent component analysis

Independent Component Analysis (ICA) is one of the fastest growing fields in the area of neural networks and signal processing. Blind Source Separation (BSS) is one of the applications of ICA. In this paper, ICA has been used for separating unknown source signals. BSS is used to extract independent signal components from their observed linear mixtures at an array of sensors. Various statistical techniques based on information theoretic and algebraic approaches exist for performing ICA. In this paper, we have used an objective function based on independence criterion of the signals. Optimisation of this objective function yields a neural algorithm along with a non-linear function for signal separation. Performance of the algorithm for artificially generated signals as well as audio signals has been evaluated.     

基于独立分量分析和遗传算法的图象分离方法研究与实现

在深入分析独立分量分析技术的基础上，针对常规数值求解方法容易陷入局部最优解的问题，提出了一种基于遗传算法和独立分量分析相结合的盲源分离新算法．通过对图象信号分离仿真试验表明，采用最佳保留机制和移民方式的动态补充子代个体操作，在一定的群体规模和遗传代数的情况下，该方法能实现信号的盲分离，并可获得全局最优解．对超高斯信号和亚高斯信号的混合信号，与扩展信息最大化方法相比，该方法可获得更好的分离效果。     

An Efficient Measure of Signal Temporal Predictability for Blind Source Separation

An efficient measure of signal temporal predictability is proposed, which is referred to as difference measure. We can prove that the difference measure of any signal mixture is between the maximal and minimal difference measure of the source signals. Previous blind source separation (BSS) problem is changed to a generalized eigenproblem by using Stone’s measure. However, by using difference measure, the BSS problem is furthermore simplified to a standard symmetric eigenproblem. And the separation matrix is the eigenvector matrix, which can be solved by using principal component analysis (PCA) method. Based on difference measure, a few efficient algorithms have been proposed, which are either in batch mode or in on-line mode. Simulations show that difference measure is competitive with Stone’s measure. Especially, the on-line algorithms derived from difference measure have better performance than those derived from Stone’s measure.     

Blind separation of non-stationary sources using continuous density hidden Markov models

Blind source separation (BSS) has attained much attention in signal processing society due to its ‘blind’ property and wide applications. However, there are still some open problems, such as underdetermined BSS, noise BSS. In this paper, we propose a Bayesian approach to improve the separation performance of instantaneous mixtures with non-stationary sources by taking into account the internal organization of the non-stationary sources. Gaussian mixture model (GMM) is used to model the distribution of source signals and the continuous density hidden Markov model (CDHMM) is derived to track the non-stationarity inside the source signals. Source signals can switch between several states such that the separation performance can be significantly improved. An expectation-maximization (EM) algorithm is derived to estimate the mixing coefficients, the CDHMM parameters and the noise covariance. The source signals are recovered via maximum a posteriori (MAP) approach. To ensure the convergence of the proposed algorithm, the proper prior densities, conjugate prior densities, are assigned to estimation coefficients for incorporating the prior information. The initialization scheme for the estimates is also discussed. Systematic simulations are used to illustrate the performance of the proposed algorithm. Simulation results show that the proposed algorithm has more robust separation performance in terms of similarity score in noise environments in comparison with the classical BSS algorithms in determined mixture case. Additionally, since the mixing matrix and the sources are estimated jointly, the proposed EM algorithm also works well in underdetermined case. Furthermore, the proposed algorithm converges quickly with proper initialization.     

基于递归神经网络结构的非平稳信号自适应盲分离

基于递归网络分离结构并利用时间相关的评价函数，针对二输入二输出盲信号分离问题，提出了一种非平稳信号的自适应盲分离算法。该算法计算量小，可根据输出信号能量大小有选择地更新分离系数。并可扩展到多输入多输出盲分离问题。仿真验证对声音等非平稳信号具有良好的分离效果。     

Blind source separation based on constant modulus criterion and signal mutual information

We address the problem of adaptive blind source separation (BSS) from instantaneous multi-input multi-output (MIMO) channels. It is known that the constant modulus (CM) criterion can be used to extract unknown source signals. However, the existing CM-based algorithms normally extract the source signals in a serial manner. Consequently, the accuracy in extracting each source signal, except for the first one, depends on the accuracy of previous source extraction. This estimation error propagation (accumulation) will cause severe performance degradation. In this letter, we propose a new adaptive separation algorithm that can separate all source signals simultaneously by directly updating the separation matrix. The superior performance of the new algorithm is demonstrated by simulation examples.     

Fast nonlinear autocorrelation algorithm for source separation

Independent component analysis (ICA) and blind source separation (BSS) methods have been used for pattern recognition problems. It is well known that ICA and BSS depend on the statistical properties of original sources or components, such as non-Gaussianity. In the paper, using a statistical property—nonlinear autocorrelation and maximizing the nonlinear autocorrelation of source signals, we propose a fast fixed-point algorithm for BSS. We study its convergence property and show that its convergence speed is at least quadratic. Simulations by the artificial signals and the real-world applications verify the efficient implementation of the proposed method.     

基于Givens矩阵和联合非线性不相关的盲源分离新算法

针对源信号统计独立的盲源分离(Blind Source Separation,BSS)问题,提出了一种基于Givens矩阵和联合非线性不相关的盲源分离新算法.由于分离信号独立性的度量是影响算法有效性的重要因素,因此首先提出了一种改进的度量独立性的方法,该方法以独立源信号的联合非线性不相关来度量独立性;其次,结合Givens矩阵可以对分离矩阵施加正交性约束且能减少要估计参数个数的性质,将盲源分离问题转化成无约束优化问题,并利用拟牛顿法中的BFGS算法求解该无约束优化问题,得到分离矩阵;最后,通过模拟混合信号和真实语音混合信号的分离实验验证了该算法的有效性.     

From separability/identifiability properties of bilinear and linear-quadratic mixture matrix factorization to factorization algorithms

Blind source separation (BSS) and Blind Mixture Identification (BMI) methods typically concern unknown source signals, transferred through a given class of functions with unknown parameter values, which yields mixed observations. Using only these observations, BSS/BMI aims at estimating the source signals and/or mixing parameters. Most investigations concern linear instantaneous mixing functions. They contain two aspects. The first one consists in proposing general BSS/BMI principles, e.g. Independent Component Analysis, Sparse Component Analysis or Nonnegative Matrix Factorization (NMF), and/or deriving associated practical algorithms. The second aspect consists in analyzing the properties resulting from these principles. This is of utmost importance, to determine if the proposed BSS/BMI principles are guaranteed to separate the source signals and to identify the considered mixing model up to acceptable indeterminacies. These separability/identifiability analyses are even more important for nonlinear mixtures, that were shown to potentially yield higher indeterminacies. Among them, bilinear and linear-quadratic mixtures are receiving increasing attention, e.g. due to their application to remote sensing. Especially, extensions of NMF were recently proposed for them, but the resulting separability/identifiability properties were not analyzed. We here address this topic, moreover proceeding further by investigating Bilinear and Linear-Quadratic Mixture Matrix Factorization (BMMF and LQMMF) approaches without nonnegativity constraints. We especially show that, whereas nonlinearity is often considered to be a burden, it yields an essentially unique decomposition under mild conditions for BMMF. On the contrary, full LQMMF is shown to yield spurious solutions, which increases the usefulness of combining it with nonnegativity constraints in applications where data meet these constraints. Algorithms based on this framework are also defined in this paper and their performance is reported.     

A BSS-based space–time multi-channel algorithm for complex-jamming suppression

This paper considers the problem of suppressing complex-jamming, which contains sidelobe blanket jammings (SLJs), multiple near-mainlobe blanket jammings (multiple-NMLJs) and self-defensive false target jamming (SDJ). We propose a blind source separation (BSS)-based space–time multi-channel algorithm for complex-jamming suppression. The space–time multi-channel consists of spatial multiple beams and temporal multiple adjacent pulse repetition intervals (PRIs). The source signals can be separated by the BSS, owing to their statistical independence. The real target and SDJ can then be obtained by the pulse compression approach, distinguished by echo identification simultaneously. A remarkable feature of the proposed approach is that it does not require prior knowledge about real target or jammings, and it is easy to implement for engineering applications.