共查询到17条相似文献,搜索用时 187 毫秒
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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。 相似文献
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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. 相似文献
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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。 相似文献
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盲源分离(BSS)的目标就是在混合过程未知的情况下,仅仅依据观测得到的混合信号,恢复出不能直接观测的源信号。针对具有时间结构的源信号,即各个源信号分量满足空间上不相关但时间上相关,提出了一种基于二阶统计量的盲源分离方法。该方法首先对混合信号进行鲁棒预白化处理,其中依据最小描述长度准则对源信号的维数进行估计;然后通过对白化信号的时延协方差矩阵进行奇异值分解(SVD),从而实现源信号的盲分离。仿真中通过对一组语音信号的分离验证了算法的效果,并利用信号干扰比(SIR)和性能指标函数(PI)两个指标定量地对算法的性能进行了度量。 相似文献
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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. 相似文献
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语音信号在非平稳系统中是动态混合的,为了实时抑制盲源分离过程中的非平稳混合扰动,加快收敛速度,减小稳态误差,提出了一种应用PID控制原理的自适应盲源分离算法。依据一种无预处理的自适应盲源分离算法建立PID控制模型,调节学习速率,跟踪语音信号的分离过程,实时减小由非平稳混合引入的分离误差,动态更新分离矩阵。在混合矩阵缓变和突变两种情形下分别对PID参数整定和语音信号的分离进行仿真分析,结合经典算法对比提出算法的性能。仿真与对比结果表明,提出的算法适用于非平稳混合系统语音信号的分离,算法性能较经典算法有改善。 相似文献
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In this paper, we propose a maximum contrast analysis (MCA) method for nonnegative blind source separation, where both the mixing matrix and the source signals are nonnegative. We first show that the contrast degree of the source signals is greater than that of the mixed signals. Motivated by this observation, we propose an MCA-based cost function. It is further shown that the separation matrix can be obtained by maximizing the proposed cost function. Then we derive an iterative determinant maximization algorithm for estimating the separation matrix. In the case of two sources, a closed-form solution exists and is derived. Unlike most existing blind source separation methods, the proposed MCA method needs neither the independence assumption, nor the sparseness requirement of the sources. The effectiveness of the new method is illustrated by experiments using X-ray images, remote sensing images, infrared spectral images, and real-world fluorescence microscopy images. 相似文献
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Sparse representation and blind source separation of ill-posed mixtures 总被引:12,自引:0,他引:12
Bofill et al. discussed blind source separation (BSS) of sparse signals in the case of two sensors. However, as Bofill et al. pointed out, this method has some limitation. The potential function they introduced is lack of theoretical basis. Also the method could not be extended to solve the problem in the case of more than three sensors. In this paper, instead of the potential function method, a K-PCA method (combining K-clustering with PCA) is proposed. The new method is easy to be used in the case of more than three sensors. It is easy to be implemented and can provide accurate estimation of mixing matrix. Some criterion is given to check the effect of the mixing matrix A . Some simulations illustrate the availability and accuracy of the method we proposed. 相似文献
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利用欠定盲源分离情况下稀疏源信号具有直线聚类的特点,提出了一种估计混叠矩阵的新方法。通过对混叠信号进行标准化处理,使混叠信号形成球形簇,将线性聚类转变成致密聚类;利用蚁群聚类算法对其进行搜索得到聚类中心,从而获得对混叠矩阵的精确估计。该方法能实现源信号数目未知情况下的欠定盲源分离,且能推广到三路或更多路观测信号的情况。对语音信号的仿真结果证明,该方法能精确地分离和恢复原始信号。 相似文献
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Two-microphone separation of speech mixtures. 总被引:1,自引:0,他引:1
Michael Syskind Pedersen DeLiang Wang Jan Larsen Ulrik Kjems 《Neural Networks, IEEE Transactions on》2008,19(3):475-492
Separation of speech mixtures, often referred to as the cocktail party problem, has been studied for decades. In many source separation tasks, the separation method is limited by the assumption of at least as many sensors as sources. Further, many methods require that the number of signals within the recorded mixtures be known in advance. In many real-world applications, these limitations are too restrictive. We propose a novel method for underdetermined blind source separation using an instantaneous mixing model which assumes closely spaced microphones. Two source separation techniques have been combined, independent component analysis (ICA) and binary time - frequency (T-F) masking. By estimating binary masks from the outputs of an ICA algorithm, it is possible in an iterative way to extract basis speech signals from a convolutive mixture. The basis signals are afterwards improved by grouping similar signals. Using two microphones, we can separate, in principle, an arbitrary number of mixed speech signals. We show separation results for mixtures with as many as seven speech signals under instantaneous conditions. We also show that the proposed method is applicable to segregate speech signals under reverberant conditions, and we compare our proposed method to another state-of-the-art algorithm. The number of source signals is not assumed to be known in advance and it is possible to maintain the extracted signals as stereo signals. 相似文献