共查询到20条相似文献,搜索用时 531 毫秒
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基于EMD和ICA的单通道语音盲源分离算法 总被引:1,自引:0,他引:1
针对单通道语音信号盲分离的问题,结合盲源分离和经验模式分解的优点.提出了一种基于经验模式分解的单通道语音信号源数估计和盲源分离方法。对语音混合信号进行经验模式分解,利用贝叶斯算法估计语音源数目,根据源信号数目重组多通道语音混合信号,并采用独立分量分析实现语音信号的盲分离。仿真实验表明,使用此法能有效地估计通道语音信号源数和分离盲源。 相似文献
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介绍了单通道混合信号的概念及盲源分离的现状,对实时线性混叠盲分离方法展开研究,分类探讨基于变换域滤波、多参数联合估计、符号序列与信道参数联合估计以及多维映射的单通道混合信号盲分离方法,分析比较各类盲源分离方法的处理对象、前提条件和优缺点,并进行了总结.最后通过仿真实验分析了基于粒子滤波与编码辅助的单通道盲分离方法中粒子数目、编码方式、源信号幅度比和频差等对分离算法性能的影响. 相似文献
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针对低轨星座系统间同频干扰问题,从信号分割角度,提出了基于单通道盲源分离的干扰减缓方法。该方法应用了单通道盲源分离算法,先通过奇异谱分析算法对地球站观测信号进行处理,构造多维轨迹矩阵;再利用快速固定点算法将干扰信号和受扰信号分离。以OneWeb和Starlink系统为例,通过所提方法将OneWeb地球站接收到的OneWeb、Starlink以及噪声的混合信号进行分解,并利用相关系数和均方根误差评估了方法的有效性。结果表明,原始信号与分离信号之间的相关系数都在0.8以上,所提方法能有效提取有用信号,减缓低轨星座系统间干扰。 相似文献
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Blind separation of speech mixtures via time-frequency masking 总被引:10,自引:0,他引:10
Binary time-frequency masks are powerful tools for the separation of sources from a single mixture. Perfect demixing via binary time-frequency masks is possible provided the time-frequency representations of the sources do not overlap: a condition we call W-disjoint orthogonality. We introduce here the concept of approximate W-disjoint orthogonality and present experimental results demonstrating the level of approximate W-disjoint orthogonality of speech in mixtures of various orders. The results demonstrate that there exist ideal binary time-frequency masks that can separate several speech signals from one mixture. While determining these masks blindly from just one mixture is an open problem, we show that we can approximate the ideal masks in the case where two anechoic mixtures are provided. Motivated by the maximum likelihood mixing parameter estimators, we define a power weighted two-dimensional (2-D) histogram constructed from the ratio of the time-frequency representations of the mixtures that is shown to have one peak for each source with peak location corresponding to the relative attenuation and delay mixing parameters. The histogram is used to create time-frequency masks that partition one of the mixtures into the original sources. Experimental results on speech mixtures verify the technique. Example demixing results can be found online at http://alum.mit.edu/www/rickard/bss.html. 相似文献
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This paper proposes a Gaussian mixture model-based Bayesian
analysis for blind source separation of an underdetermined model that has more
sources than sensors. The proposed algorithm follows a hierarchical learning
procedure and alternative estimations for sources and the mixing matrix. The
independent sources are estimated from their posterior means, and the mixing
matrix is estimated by the maximum likelihood method. Because each source
is conditionally correlated with others in its Markov blanket, the
correlations between them are approximated by using linear response theory;
this is based on the factorized approximation to the sources' true
posteriors. In this framework, each source is modeled as a mixture of
Gaussians to fit its actual distribution. Given enough Gaussians, the
mixture model can learn any distribution. The algorithm provides a good
identification of the mixing system, and its flexibility speeds up
the convergence. The iterative learning for Gaussians leads to a parametric
density estimation for all hidden sources as well as their recovery in the
end. The major advantages of this algorithm are its flexibility and its fast
convergence. Simulations using synthetic data validate the effectiveness
of the algorithm. 相似文献
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Aiming to the estimation of source numbers, mixing matrix and separation of mixing signals under underdetermined case, the article puts forward a method of underdetermined blind source separation (UBSS) with an application in ultra-wideband (UWB) communication signals. The method is based on the sparse characteristic of UWB communication signals in the time domain. Firstly, finding the single source area by calculating the ratio of observed sampling points. Then an algorithm called hough-windowed method was introduced to estimate the number of sources and mixing matrix. Finally the separation of mixing signals using a method based on amended subspace projection. The simulation results indicate that the proposed method can separate UWB communication signals successfully, estimate the mixing matrix with higher accuracy and separate the mixing signals with higher gain compared with other conventional algorithms. At the same time, the method reflects the higher stability and the better noise immunity. 相似文献
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《IEEE transactions on circuits and systems. I, Regular papers》2006,53(10):2287-2298
This paper presents a gradient-based method for simultaneous blind separation of arbitrarily linearly mixed source signals. We consider the regular case (i.e., the mixing matrix has full column rank) as well as the ill-conditioned case (i.e., the mixing matrix does not have full column rank). We provide one necessary and sufficient condition for the identifiability of simultaneous blind separation. According to our identifiability condition and the existing general identifiability condition, all source signals are separated into two categories: separable single sources and inseparable mixtures of several single sources. A sufficient condition is also derived for the existence of optimal partition of the mixing matrix which leads to a unique maximum set of separations. One sufficient condition is proved to show that each maximum partition of the mixing matrix corresponds to a unique class of separated signals and as a result we can determine the number of maximum partitions from the classes of outputs under different separation matrices. For sub-Gaussian or super-Gaussian source signals, a cost function based on fourth-order cumulants is introduced to simultaneously separate all separable single sources and all inseparable mixtures. By minimizing the cost function, a gradient-based method is developed. Finally, simulation results show the effectiveness of the present method. 相似文献
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To solve the problem of mixing matrix estimation for underdetermined blind source separation (UBSS) when thenumber of sources is unknown, this paper proposed a novel mixing matrix estimation method based on averageinformation entropy and cluster validity index (CVI). Firstly, the initial cluster center is selected by using fuzzy C-means (FCM) algorithm and the corresponding membership matrix is obtained, and then the number of clusters isobtained by using the joint decision of CVI and average information entropy index of membership matrix, thenmultiple cluster number estimation results can be obtained by using multiple CVIs. Then, according to the results ofthe number of multiple clusters estimation, the number of radiation sources is determined according to the principleof the subordination of the minority to the majority. The cluster center vectors obtained from the clustering operationof the estimated number of radiation sources are fused, that is the mixing matrix is estimated based on the degree ofsimilarity of the cluster center vectors. When the source signal is not sufficiently sparse, the time-frequency singlesource detection processing can be combined with the proposed method to estimate the mixing matrix. Theeffectiveness of the proposed method is validated by experiments. 相似文献
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This paper considers the problem of blindly separating sub- and super-Gaussian sources from underdetermined mixtures. The underlying sources are assumed to be composed of two orthogonal components: one lying in the rowspace and the other in the nullspace of a mixing matrix. The mapping from the rowspace component to the mixtures by the mixing matrix is invertible using the pseudo-inverse of the mixing matrix. The mapping from the nullspace component to zero by the mixing matrix is noninvertible, and there are infinitely many solutions to the nullspace component. The latent nullspace component, which is of lower complexity than the underlying sources, is estimated based on a mean square error (MSE) criterion. This leads to a source estimator that is optimal in the MSE sense. In order to characterize and model sub- and super-Gaussian source distributions, the parametric generalized Gaussian distribution is used. The distribution parameters are estimated based on the expectation-maximization (EM) algorithm. When the mixing matrix is unavailable, it must be estimated, and a novel algorithm based on a single source detection algorithm, which detects time-frequency regions of single-source-occupancy, is proposed. In our simulations, the proposed algorithm, compared to other conventional algorithms, estimated the mixing matrix with higher accuracy and separated various sources with higher signal-to-interference ratio. 相似文献
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在信源数目未知的欠定盲源分离问题中,精确地估计混合矩阵是具有挑战性的问题。针对现有方法在病态条件下(某些混合向量的方向接近)不能准确估计信源数目、易受离群点干扰的不足,提出了一种基于方向性模糊C-means与K-means的混合矩阵估计方法。该方法首先通过方向性模糊C-means对观测信号进行预聚类,通过预聚类可以实现:1) 根据聚类有效性指标值的收敛点确定信源数目;2)根据隶属度矩阵排除离群点;3)确定K-means的初始聚类点。最后使用K-means并利用预聚类确定的信源数目及初始聚类点实现混合矩阵估计。仿真结果表明提出的方法具有更优的混合矩阵估计性能。 相似文献
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The linear mixing model has been considered previously in most of the researches which are devoted to the blind source separation (BSS) problem. In practice, a more realistic BSS mixing model should be the non-linear one. In this paper, we propose a non-linear BSS method, in which a two-layer perceptron network is employed as the separating system to separate sources from observed non-linear mixture signals. The learning rules for the parameters of the separating system are derived based on the minimum mutual information criterion with conjugate gradient algorithm. Instead of choosing a proper non-linear functions empirically, the adaptive kernel density estimation is used in order to estimate the probability density functions and their derivatives of the separated signals. As a result, the score function of the perceptron’s outputs can be estimated directly. Simulations show good performance of the proposed non-linear BSS algorithm. 相似文献