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Blind source separation of single-channel mixed recording is a challenging task that has applications in the fields of speech, audio and bio-signal processing. Numerous blind source separation methods are commonly used for blind separation of single input multiple output. However, the priori knowledge of the signal is assumed to be known or the main channels selected from multi-channel output are not self-adaptive and automatic. Presented in this paper is a new method based on dimensionality reduction of ensemble empirical mode decomposition (EEMD), and ICA does not rely on such assumptions. The EEMD represents any time-domain signal as the sum of a finite set of oscillatory components called intrinsic mode functions (IMFs). ICA finds the independent components by maximizing the statistical independence of the dimensionality reduction IMFs. Principal component analysis (PCA) is applied to reduce dimensions of IMFs. The separated performance of EEMD-PCA-ICA algorithm is compared with EEMD-ICA through simulations, and experimental results show EEMD-PCA-ICA algorithm outperforms EEMD-ICA with higher cross-correlation and lower relative root mean squared error (RRMSE). 相似文献
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Method for eliminating mode mixing of empirical mode decomposition based on the revised blind source separation 总被引:2,自引:0,他引:2
Since mode mixing of empirical mode decomposition (EMD) is mainly caused by the intermittence and noise, we propose a novel method to eliminate mode mixing of EMD based on the revised blind source separation. To this aim, an optimal morphological filter is employed to eliminate the noise. As a result, the component of mode mixing caused by noise is suppressed. Furthermore, the de-noised signal is decomposed into different intrinsic mode function (IMF) components through the EMD algorithm. Since it is impossible to apply blind source separation to a single channel signal directly, the IMF component, which has mode mixing is chosen and reconstructed in the phase space. Following that, the equivalent hypothetical signals are obtained. Finally, an improved fixed-point algorithm based on independent component analysis (ICA) is introduced to separate the overlapping components. The analysis of simulation and practical application demonstrates that our proposed method can effectively tackle the mode mixing problem of EMD. 相似文献
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针对时频域部分重叠的多个跳频通信信号共信道盲分离问题,提出了一种新的共信道盲分离算法SCBSS(Single Channel Blind Source Separation)。首先,重新定义多分辨奇异谱分析(Multi-resolution Singular Spectrum Analysis,MRSSA)算法,利用其冗余性来重构伪多输入输出模型;接着引入独立分量分析算法用于提取感兴趣的独立分量。仿真结果验证了所提算法分离多个正交相移键控(Quadrature Phase Shift Keying,QPSK)调制的时频域部分重叠跳频通信信号的有效性和鲁棒性,且不需要任何先验。 相似文献
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为了降低语音信号盲源分离算法的延时,提高其准确性和稳定性,本文结合传统盲源分离技术和深度神经网络的优势,提出了一种基于ICA独立分量分析和复数神经网络的二麦阵列盲源分离技术。本文将复数递归神经网络和独立分量分析方法有机融合,提出一种基于时频域的双通道复数神经网络,同时解决了独立分量分析中的排列问题。所提方法利输入混合信号利用复数域神经网络计算初始化分离矩阵,神经网络输出采用复数域形式,利用复数学习标签估计复数矩阵,然后采用独立分量分析方法获得目标分离矩阵。实验数据表明,所提方法相较于其它独立分量分析方法提高了盲源分离的实时性和准确性。 相似文献
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针对非协作通信中成对载波多址(Paired Carrier Multiple Acess,PCMA)信号的盲分离问题,提出了一种基于独立分量分析(Independent component analysis,ICA)的单通道盲分离算法。首先对接收到的单路PCMA信号进行参数估计得到其残余载波频率,再对其处理得到两路基带混合信号,最后利用ICA算法分离出源基带信号。该算法在未知两个卫星地面站发送信号的情况下,从接收到的PCMA信号中恢复出两路源基带信号。仿真实验表明,本文算法在信噪比为-10dB时仍具有良好的分离效果,两路基带信号的波形相似系数可分别达到0.94与0.86以上。 相似文献
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独立分量分析(ICA)是一种通过最大化多维观察向量元素的统计独立性寻找一个线性变换的统计方法,其作为有效的盲源分离技术是信号处理领域的热点。提出了一种基于峰度的快速ICA算法,此算法常用于盲信号分离和特征抽取。先从峰度的定义入手说明峰度作为代价函数的可行性,并详细介绍如何将神经网络学习规则转换成固定点准则,从而使得算法简单,且不依赖任何人为定义的参数。选取3个非高斯独立向量作为信号源进行Matlab仿真,分离效果良好。 相似文献
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In recent years, blind source separation (BSS) by independent component analysis (ICA) has been drawing much attention because of its potential applications in signal processing such as in speech recognition systems, telecommunication and medical signal processing. In this paper, two algorithms of independent component analysis (fixed-point IC,4 and natural gradient-flexible ICA) are adopted to extract human epileptic feature spikes from interferential signals. Experiment results show that epileptic spikes can be extracted from noise successfully. The kurtosis of the epileptic component signal separated is much better than that of other noisy signals. It shows that ICA is an effective tool to extract epileptic spikes from patients' electroencephalogram EEG and shows promising application to assist physicians to diagnose epilepsy and estimate the epileptogenic region in clinic. 相似文献
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提出基于总体经验模态分解(EEMD)血流细分法提高血流超声多普勒信号提取精度.首先估计辅助分析所需的白噪声幅度,进而用EEMD得到无模态混叠的本征模态函数(IMF)组,最后分离出血流信号的IMF.将本方法应用于计算机仿真和人体实测超声多普勒信号,并与高通滤波器法、原EMD法和EMD细分法比较.结果表明本文方法,提取的血流信号精度最高,特别对WBSR=70dB的混合信号,其精度比上述方法分别提高35%、38%及17%. 相似文献
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稀疏分量分析在欠定盲源分离问题中的研究进展及应用 总被引:3,自引:0,他引:3
伴随着国内外对盲源分离问题研究的日益深入,在独立分量分析等经典算法之外逐步发展出了许多新的算法.稀疏分量分析就是其中有效的方法之一,它利用信号的稀疏分解,克服了独立分量分析非欠定性的要求,解决了欠定情况下的盲源分离问题.本文将以稀疏分量分析为主要对象,归纳总结了近期的研究进展. 相似文献
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基于ICA(独立成分分析:Independent Component Analylsis)原则,给出一种盲信号分离的快速学习算法.通过寻求观测变量线性组合的四阶累积量(即kurtosis系数)局部极值,得出该算法的模型和步骤.将该算法用于盲信号分离实验,实验结果表明,该算法在盲信号分离和信号特征提取方面具有收敛速度快、无需动态参数等优点.该算法能有效地分离出任意分布的非高斯盲源信号的各个独立成分,是信号处理的一种新的、高效可靠的方法. 相似文献
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利用独立分量分析(ICA)的自适应粒子群(APSO)算法对因传输等过程而引起的多幅灰度图像混叠进行盲分离,针对图像盲分离提出了一种基于改进的APSO的盲源分离算法并将其应用于分离模糊灰度图像。利用峰度和负熵分别作为粒子群算法的第一和第二适应度函数根据其高斯性原理作为独立性判别标准对分离矩阵进行自适应更新。分析比较不同盲分离算法对图像分离的收敛性,仿真结果证明改进的自适应粒子群算法能够很好地分离图像且计算性能指标优越,收敛效果好。 相似文献
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一种自适应算法的语音信号盲分离 总被引:1,自引:0,他引:1
盲信号处理算法主要有批处理算法和自适应算法两类,本文导出了一种批处理和自适应相结合的快速独立分量分析(Fast Independent Component Analysis, Fast ICA)算法,将该算法应用于语音信号盲分离处理,通过综合实验,从分离前后的波形、频谱图和主要评价参数说明该算法具有良好的信号分离效果。与扩展联合对角化(The Joint Approximative Diagonalization ofEigenmatrix,JADE)算法和自然梯度(Natural Gradient,NG)算法比较, fast ICA算法具有更好的分离效果。 相似文献
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针对现有的独立成分分析法分离混合混沌信号精度不理想的问题,提出了一种新的混沌信号盲分离方法。该方法以求解最优解混矩阵为目标,利用峭度构造目标函数,将混沌信号的盲源分离转化为一个优化问题,并用萤火虫算法求解。同时,通过预白化和正交矩阵的参数化表示降低优化问题的维数,能有效提高分离精度。仿真结果表明,无论是处理混合的混沌映射信号还是混合的混沌流信号,该方法都能快速收敛,并且其分离精度在各项实验中都优于独立成分分析法等现有的盲源分离方法。 相似文献