语音增强的计算机仿真

研究关于盲源分离的特征向量分离算法在语音增强的应用,传统的方法对混合的语音信号很难进行有效的分离,而在实际中很多场合都需要对语音信号进行增强.为消除噪音,提高清晰度,使用的盲源分离算法却正能实现传统方法难以实现的技术.运用一种盲源分离的特征向量分离算法来进行语音增强,并且对实际的两个语音信号运用该算法进行了混合语音信号的分离增强实验,利用MATLLAB软件对混合语音信号进行了盲源分离的特征向量分离算法的仿真,可从混合语音信号分离出了两个原始语音信号.证明了盲源分离算法应用于语音分离的可行性,为盲源分离应用于语音增强提供了参考依据.     

基于准正交原理的多信源少观测源的盲语音信号分离

信号源个数多于观测信号个数情况下的盲源分离问题是盲信号分离中的一个难题,也是一个很实际的问题。论文在A.Hyvrinen提出的一种基于准正交原理的盲分离算法基础上,指出当混合矩阵的基矢量不满足准正交性时,可以对观测信号预白化,使混合矩阵的基矢量的准正交性得以很大提高。然后将此方法用于多信源少观测源情况下的混合语音信号分离。实验分为两个过程:(1)估计混合矩阵;(2)用最大后验概率的估计方法分离源语音信号。实验结果证明了该算法能够有效用于高维情况下多信源少观测源的盲语音信号分离。     

基于Givens变换矩阵的时间结构信号盲源分离新算法

对于时间结构信号的盲源分离(Blind Source Separation,BSS),独立成分分析(Independent Component Analysis,ICA)是十分有效的方法。在对观测信号白化处理后,ICA的关键是寻找去除高阶相关性的正交分离矩阵。鉴于任意维数正交矩阵可以表示为Givens变换矩阵的乘积,提出了一种新的时间结构信号盲源分离算法。首先,利用Givens变换矩阵参数化表示正交分离矩阵,减少了要估计参数的个数;其次,以多步时延协方差矩阵的联合近似对角化为目标函数,将盲源分离问题转化为无约束优化问题,并利用拟牛顿法中的BFGS算法对Givens变换矩阵中的参数进行估计,得到分离矩阵;最后,以实际的混合语音信号分离做仿真实验,验证了该算法对时间结构信号的盲源分离是有效的。     

基于时变自回归模型的FAJD盲源分离算法

为实现多高斯源和相关源信号的盲分离,在快速近似联合对角化(FAJD)算法的基础上,将故障诊断领域的时变自回归理论成功地应用于相关源信号的盲分离和多高斯源信号的盲分离.首先采用时变自回归模型(TVAR)对源信号建模,并通过白化预处理使得建模后的源信号具有可联合对角化的结构;然后,通过基函数加权和的方法将时变参数近似为已知基函数的加权和的形式,将其变成时不变的参数,再通过递推最小二乘法求解出模型系数矩阵组;最后,将所求出的系数矩阵组作为快速近似联合对角化的目标矩阵组,通过FAJD算法实现混合信号的分离.Matlab仿真实验验证了所提出的算法对于相关源信号和多高斯源信号的分离是行之有效的.由于算法中TVAR模型的优良特性,此算法非常适用于混合通信信号的盲分离.     

一种采用径向基神经网络的盲源分离算法

针对传统盲源分离算法的计算复杂问题,提出一种基于径向基(RBF)神经网络盲源分离算法,用K均值聚类算法对中心值和宽度值进行确定,用最大熵为代价函数来确定权值,所用的代价函数保证了网络的输出尽可能独立,使信号能正确地分离.仿真中,用于对线性混合信号进行盲源分离,并与最大熵(ME)算法进行比较.结果表明,盲源分离算法能减少分离时间和提高分离效率,并且能大大降低计算量,比ME算法更好.     

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.     

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.     

基于品质函数估计的非线性盲源分离算法

研究非线性盲源信号分离优化问题。由于混合信号同时包含超高斯和亚高斯信号且混合信号具有很强的非线性时,传统的非线性盲源分离算法中对于品质函数的选取一般都是通过经验,现有算法难以取得理想的分离效果。在Pearson模型的基础上提出了一种新的估计品质函数的方法,算法能够成功地估计出次高斯(sub-Gaussian)和超高斯(super-Gaussi-an)混合信号的品质函数,同时克服了Pearson模型对同类信号只能估计得到相同的品质函数的缺陷,提高了算法的估计精度。通过在MATLAB仿真验证了算法的可行性和有效性,成功估计出源信号的品质函数且实现了非线性盲源分离。     

基于时频分布的非平稳信号盲分离算法

针对传统的盲源分离方法往往忽略信号非平稳性的问题,基于从瞬时线性混合模型的观测信号中分离出相互独立的源信号,并针对信号具有非平稳性,结合时频分析和盲源分离各自的特点,对非平稳信号盲分离进行了研究,并提出了一种新的具有不同空间时频分布的非平稳盲分离算法.仿真实验表明,通过采用维纳全时频域搜索来寻找局部最大值的平滑伪Wigner-Ville分布,该算法可以抑制交叉项而且能够保持时频聚集性,并达到了很好的分离效果.     

基于矩阵对角化的盲源分离算法研究

盲源分离是从观测信号中恢复源信号的一种有效方法,目前已成为信号处理领域的研究热点。首先对三种盲源分离的算法进行分析,它们是:四阶盲辨识(FOBI)、特征矩阵的联合近似对角化(JADE)、二阶盲辨识(SOBI)。分析表明这些算法均有各自的不足,而另一方面,它们都是通过矩阵对角化实现盲源分离的。一个很自然的想法是将这些算法结合起来,以提高盲源分离的性能。仿真结果表明,JADE法和SOBI法的结合可以获得不错的盲分离效果。     

Blind source separation with unknown and dynamically changing number of source signals

The contrast function remains to be an open problem in blind source separation (BSS) when the number of source signals is unknown and/or dynamically changed. The paper studies this problem and proves that the mutual information is still the contrast function for BSS if the mixing matrix is of full column rank. The mutual information reaches its minimum at the separation points, where the random outputs of the BSS system are the scaled and permuted source signals, while the others are zero outputs. Using the property that the transpose of the mixing matrix and a matrix composed by m observed signals have the indentical null space with probability one, a practical method, which can detect the unknown number of source signals n, ulteriorly traces the dynamical change of the sources number with a few of data, is proposed. The effectiveness of the proposed theorey and the developed novel algorithm is verified by adaptive BSS simulations with unknown and dynamically changing number of source signals.     

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.     

基于核估计的超定混合共轭盲信号分离方法

当混合信号的个数多于源信号时,盲源分离模型中的混合矩阵被描述为一个超定矩阵,因此不能直接通过估计逆矩阵的方法来得到分离矩阵。针对该线性超定混合情况提出了一种基于共轭梯度的盲源分离方法。该方法基于最小互信息准则,通过对行满秩分离矩阵的奇异值分解而引入了超定盲源分离的代价函数。利用共轭梯度优化算法推导出了迭代计算分离矩阵的更新公式。在每次迭代计算中,利用随机变量概率密度估计的核函数法在线估计分离信号的评价函数。避免了诸多传统盲分离算法中只能凭经验选取特定的非线性函数来代替评价函数的问题。仿真结果验证了所提算法的有效性。     

Blind Separation of Mutually Correlated Sources Using Precoders

This paper studies the problem of blind source separation (BSS) from instantaneous mixtures with the assumption that the source signals are mutually correlated. We propose a novel approach to BSS by using precoders in transmitters. We show that if the precoders are properly designed, some cross-correlation coefficients of the coded signals can be forced to be zero at certain time lags. Then, the unique correlation properties of the coded signals can be exploited in receiver to achieve source separation. Based on the proposed precoders, a subspace-based algorithm is derived for the blind separation of mutually correlated sources. The effectiveness of the algorithm is illustrated by simulation examples.      

Mixing matrix estimation using discriminative clustering for blind source separation

Mixing matrix estimation in instantaneous blind source separation (BSS) can be performed by exploiting the sparsity and disjoint orthogonality of source signals. As a result, approaches for estimating the unknown mixing process typically employ clustering algorithms on the mixtures in a parametric domain, where the signals can be sparsely represented. In this paper, we propose two algorithms to perform discriminative clustering of the mixture signals for estimating the mixing matrix. For the case of overdetermined BSS, we develop an algorithm to perform linear discriminant analysis based on similarity measures and combine it with K-hyperline clustering. Furthermore, we propose to perform discriminative clustering in a high-dimensional feature space obtained by an implicit mapping, using the kernel trick, for the case of underdetermined source separation. Using simulations on synthetic data, we demonstrate the improvements in mixing matrix estimation performance obtained using the proposed algorithms in comparison to other clustering methods. Finally we perform mixing matrix estimation from speech mixtures, by clustering single source points in the time-frequency domain, and show that the proposed algorithms achieve higher signal to interference ratio when compared to other baseline algorithms.     

A new concept for separability problems in blind source separation

The goal of blind source separation (BSS) lies in recovering the original independent sources of a mixed random vector without knowing the mixing structure. A key ingredient for performing BSS successfully is to know the indeterminacies of the problem-that is, to know how the separating model relates to the original mixing model (separability). For linear BSS, Comon (1994) showed using the Darmois-Skitovitch theorem that the linear mixing matrix can be found except for permutation and scaling. In this work, a much simpler, direct proof for linear separability is given. The idea is based on the fact that a random vector is independent if and only if the Hessian of its logarithmic density (resp. characteristic function) is diagonal everywhere. This property is then exploited to propose a new algorithm for performing BSS. Furthermore, first ideas of how to generalize separability results based on Hessian diagonalization to more complicated nonlinear models are studied in the setting of postnonlinear BSS.     

Adaptive weighted orthogonal constrained algorithm for blind source separation

Blind source separation (BSS) consists of recovering the statistically independent source signals from their linear mixtures without knowing the mixing coefficients. Pre-whitening is a useful pre-processing technique in BSS. However, BSS algorithms based on the pre-whitened data lack the equivariance property, one of the significant properties in BSS. By transforming the pre-whitening into a weighted orthogonal constraint condition, this paper proposes a new definition of the contrast function. In light of the constrained optimization method, various weighted orthogonal constrained BSS algorithms with equivariance property are developed. Simulations on man-made signals and practical speech signals show the proposed weighted orthogonal constrained BSS algorithms have better separation ability, convergent speed and steady state performance.     

A parametric density model for blind source separation

In this paper, a parametric mixture density model is employed to be the source prior in blind source separation (BSS). A strict lower bound on the source prior is derived by using a variational method, which naturally enables the intractable posterior to be represented as a gaussian form. An expectation-maximization (EM) algorithm in closed form is therefore derived for estimating the mixing matrix and inferring the sources. Simulation results show that the proposed variational expectation-maximization algorithm can perform blind separation of not only speech source of more sources than mixtures, but also binary source of more sources than mixtures.     

适用于时变系统的在线盲源分离算法研究

针对传统EASI算法收敛速率与稳态误差之间的矛盾,提出了一种基于估计函数期望的步长自适应算法（New Adaptive EASI）,为了使这种算法能够更好地解决时变系统中不同条件下的盲源分离问题,提高信号的分离精度,建立了一种混合矩阵变化的在线检测机制,并将这种在线检测机制加入步长自适应算法中,对算法进行了改进。仿真实验表明,这种改进的步长自适应算法能够提高盲源分离初始阶段或是信道变化后分离初始阶段的信号恢复质量,解决源信号为非零均值信号时的盲源分离问题,并且能够准确地在线估计源信号的个数,实现信源数变化条件下的盲源分离。