多通道同步信号采集与盲源分离研究

扩展了麦克风语音采集电路和较大容量的存储器,基于凌阳16位单片机(SPCE061A)实现了多通道语音信号的实时同步采集。对采集的信号应用FastICA独立分量分析算法进行盲源分离,基于DTW算法对分离的语音进行识别,发现强噪声情况下识别结果良好,对语音识别与应用具有积极意义。     

含噪混合数据中相关源信号的盲分离

本文在假设时间白噪声的基础上,通过对阵列施加一个较弱的旋转不变约束提出了一种针对相关源信号盲分离的方法。与现有方法相比,该方法不需要额外的源信号约束条件和噪声自相关矩阵的知识,因此具有较广的使用范围和较强的稳健性。另外,该方法较低的计算复杂度易于其工程实现。计算机仿真结果表明,本文提出的方法对于相关源信号的盲分离具有较好的效果。     

一种混沌信号的盲分离方法

多个混合混沌信号的分离，在混沌及其应用中是一个重要的问题.提出了一种线性混合的混沌信号的瞬时盲分离方法，它利用了各个混沌信号源之间的互不相关性，在未知混合矩阵和混沌方程的情况下，通过求解特征向量的方法从观测量中直接估计出解混合矩阵，以重构出源混沌信号.仿真结果表明，即使在低信噪比情况下，该方法仍可以有效地从噪声背景中分离出多个混合的混沌信号. 关键词： 混沌 盲分离 信噪比 特征向量     

一种基于人工蜂群算法的混沌信号盲分离方法

混沌信号所固有的非周期、宽带频谱和对初值极度敏感等特性使得对这类信号进行盲分离极为困难. 针对这一问题, 提出一种新的盲分离方法, 该方法通过相空间重构来构造代价函数, 将混沌信号的盲分离转化为一个无约束优化问题, 并利用人工蜂群算法进行求解. 不同于现有的独立成分分析方法仅使用混合信号的统计特性来解决分离问题, 该方法能充分利用混合信号内在的动态特性, 因而在处理混沌信号这种确定性信号时能获得更好的分离效果. 此外, 正交矩阵的参数化表示有效地降低了盲分离问题的复杂性, 使优化过程能快速收敛. 实验结果表明, 该方法具有较快的收敛速度和较高的数值精度, 在分离混沌信号时其整体性能优于现有的几种盲分离方法. 同时, 在分离混沌-高斯混合信号的实验中该方法也展现出优异良好的性能, 这表明该文的方法有应用潜力.     

非线性混合模式的语音盲分离算法

本文针对FIR非线性混合模型，基于最大熵算法，提出了一种以高斯混合模式概率密度函数估计替代传统对数化概率密度估计的盲分离算法，以偶函数为非线性激活函数，采用最大期望（EM）迭代算法推导了分离算法的权向量迭代公式，通过模拟仿真实验结果与传统的最大熵和高阶累积量方法比较，新算法提高了收敛速度，并有效地完成了非线性语音分离任务，抑制了干扰语音信号的影响，提高了输出信噪比。     

低复杂度的盲源分离和去混响联合优化方法

提出了一种低复杂度的基于加权预测误差(WPE)的独立低秩矩阵分析(ILRMA)方法。与现有的WPE-ILRMA方法把预测矩阵当成一个整体来处理不同,所提方法将预测矩阵展开来推导代价函数,利用不同声源的混合滤波器和分离滤波器之间的正交性简化代价函数的优化过程,进而以更低的计算复杂度对混合信号去混响。通过利用解耦预测矩阵和分离滤波器之间的关系,所提方法将维数较大的矩阵求逆转化为维数较小的矩阵求逆,从而取得了比WPE-ILMRA方法更低的计算复杂度。在最大似然框架下推导了所提方法的代价函数,并采用坐标梯度下降算法来估计参数。实验结果表明,所提方法能以更低的计算复杂度和更高的稳定性取得与WPE-ILRMA方法相似的分离性能。     

基于盲分离技术的水下多源环形阵定位方法研究

根据水下自动探测的工程实际技术发展需求,本文基于盲分离技术研究了环形阵对水下目标辐射噪声信号进行定位的方法,利用水声信号统计特性,通过构建针对水中目标的非线性函数和学习因子等,对自然梯度算法进行了改进,实船信号的盲分离实验结果表明,该方法改进了分离效果。在此基础上,建立了基于线性瞬时混合情况下的定位模型,完成了对六个水中目标的定位研究,取得了令人满意的结果。     

复杂电磁环境下的信号盲分离方法

介绍了基于盲源分离的信号处理基本思想,阐述了ICA,SOBI,AMUSE三种典型算法的原理、特点,通过仿真实验研究了这三种盲源分离算法对于常见波形信号和真实信号的分离性能及其抗噪能力,验证了算法的有效性和可行性。三种方法对于抑制干扰信号,进行信号的有效提取具有实际意义和使用价值。     

复杂电磁环境下的信号盲分离方法

介绍了基于盲源分离的信号处理基本思想,阐述了ICA,SOBI,AMUSE三种典型算法的原理、特点,通过仿真实验研究了这三种盲源分离算法对于常见波形信号和真实信号的分离性能及其抗噪能力,验证了算法的有效性和可行性。三种方法对于抑制干扰信号,进行信号的有效提取具有实际意义和使用价值。     

子带t分布的快速独立向量分析在语声盲源 分离中的应用*

为了提高独立向量分析算法在盲语声分离任务中的分离性能,降低算法计算复杂度,并改善目前尚未完全解决的顺序模糊性的问题,该文提出一种基于子带t分布的快速独立向量分析算法。在声源模型方面,该算法首先利用语声信号重尾分布的特性,假设声源概率密度函数服从t分布,同时采用子带建模的方法来增强同一声源相邻频点的高阶依赖性,进而减轻频点间的顺序不一致问题。在空间模型方面,该算法采用秩1更新的方式估计声源信号,避免矩阵求逆操作和分离矩阵的估计,从而降低计算复杂度。实验结果表明,与现有的基于独立向量分析的盲源分离算法相比,该算法能够在相同的迭代次数下取得更优的语声分离性能。     

A new regularized forward blind source separation algorithm for automatic speech quality enhancement

This paper addresses the problem of speech enhancement and acoustic noise reduction by adaptive filtering algorithms in a moving car through blind source separation (BSS) structures. In this paper we propose a new regularized forward blind source separation (RFBSS) algorithm that does not need voice activity detection (VAD) systems, and allows getting efficient speech enhancement performances with low complexity.     

Independent component analysis of photoacoustic depth profiles

Photoacoustic spectroscopy is a powerful tool to non-destructively retrieve depth related information with minimal sample preparation from heterogeneous samples. Using a phase modulated step-scan arrangement and digital signal processing a series of spectra with effectively different modulation frequencies, probing different thermal diffusion lengths within a sample, can be collected simultaneously. In this report independent component analysis is used to analyze photoacoustic data acquired from a multilayer sample in an attempt to isolate the spectra of the constituent layers. The ICA findings are compared to the approach of eliciting this information through difference spectroscopy. The results suggest that ICA may offer substantial benefits for segmentation of PAS depth profiles without user intervention. This approach can be useful in analysis of complex heterogeneous samples when one has no prior knowledge of their constituents or their distribution in the sample.     

Performance of blind source separation algorithms for fMRI analysis using a group ICA method

Independent component analysis (ICA) is a popular blind source separation technique that has proven to be promising for the analysis of functional magnetic resonance imaging (fMRI) data. A number of ICA approaches have been used for fMRI data analysis, and even more ICA algorithms exist; however, the impact of using different algorithms on the results is largely unexplored. In this paper, we study the performance of four major classes of algorithms for spatial ICA, namely, information maximization, maximization of non-Gaussianity, joint diagonalization of cross-cumulant matrices and second-order correlation-based methods, when they are applied to fMRI data from subjects performing a visuo-motor task. We use a group ICA method to study variability among different ICA algorithms, and we propose several analysis techniques to evaluate their performance. We compare how different ICA algorithms estimate activations in expected neuronal areas. The results demonstrate that the ICA algorithms using higher-order statistical information prove to be quite consistent for fMRI data analysis. Infomax, FastICA and joint approximate diagonalization of eigenmatrices (JADE) all yield reliable results, with each having its strengths in specific areas. Eigenvalue decomposition (EVD), an algorithm using second-order statistics, does not perform reliably for fMRI data. Additionally, for iterative ICA algorithms, it is important to investigate the variability of estimates from different runs. We test the consistency of the iterative algorithms Infomax and FastICA by running the algorithm a number of times with different initializations, and we note that they yield consistent results over these multiple runs. Our results greatly improve our confidence in the consistency of ICA for fMRI data analysis.     

结合盲源分离的非直单线阵多目标左右舷分辨

针对单线阵左右舷模糊问题,提出了结合盲源分离的非直单线阵多目标左右舷分辨算法。该算法首先通过离散傅里叶变换将阵列接收信号离散化为若干个窄带频谱分量,之后对每个频点的窄带数据讲行盲源分离,得到每个频点上各个来波信号的导向向量;然后通过常规波束形成对各个导向向量进行方位谱估计,并根据左右舷抑制比进行单目标左右舷分辨,确定各个频点上的来波方向;最后对所有频点上的来波方向进行聚类,得到各个真实目标的方位,从而实现多目标左右舷分辨。仿真实验中,相比常规波束形成方法(CBF)和最小方差无畸变响应算法(MVDR),该算法更准确地估计出了目标数目,且保持了较快的计算速度;海试数据处理中,该算法排除了目标镜像的干扰,准确估计出了船只目标的轨迹。仿真及海试数据处理均表明,该算法可以分辨真实目标与目标镜像,具有比CBF和MVDR算法更好的左右舷分辨能力。     

信号盲分离的非线性累加和算法

就一种基于非线性累加和的信号盲分离方法作了探讨。首先,文中针对方法中非线性函数特性对信号分离的影响从理论上作了分析,给出了加快迭代收敛的约束条件。接着,将该方法从无卷积混合信号分离的情况推广到混合卷积情形。最后通过数值模拟,给出了信号分离结果。     

Real-time blind source separation system with applications to distant speech recognition

A real-time BSS system based on DUET was developed and implemented in order to assess its potential as the front-end for a DSR engine. The system uses only two closely-spaced standard omni-directional microphones and a computer soundcard and was developed for low reverberation environments with several human speakers and different noise sources.     

A weak blind signature scheme based on quantum cryptography

In this paper, we present a weak blind signature scheme based on the correlation of EPR (Einstein-Padolsky-Rosen) pairs. Different from classical blind signature schemes and current quantum signature schemes, our quantum blind signature scheme could guarantee not only the unconditionally security but also the anonymity of the message owner. To achieve that, quantum key distribution and one-time pad are adopted in our scheme. Experimental analysis proved that our scheme have the characteristics of non-counterfeit, non-disavowal, blindness and traceability. It has a wide application to E-payment system, E-government, E-business, and etc.     

Gradient method for blind chaotic signal separation based on proliferation exponent

A new method to perform blind separation of chaotic signals is articulated in this paper, which takes advantage of the underlying features in the phase space for identifying various chaotic sources. Without incorporating any prior information about the source equations, the proposed algorithm can not only separate the mixed signals in just a few iterations, but also outperforms the fast independent component analysis(FastICA) method when noise contamination is considerable.