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这是实际环境中语音信号盲分离的最新程序代码，用于语音信号独立分量分析ICA。解压后运行，输入录制的混合语音信号即可看到结果。-this is the actual environment of the speech signal separation blind to the latest code, Voice Signal for independent component analysis ICA. Unpacked operation, the importation of record
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FastICA算法，用于信号的独立分量分析，在ICA的基础上加快了收敛速度，有更高的效率！并且增加了图象界面，使用方便！-FastICA algorithm, the signal for an independent component analysis, at the ICA on the basis of accelerating the convergence rate, a more efficient! And to increase the image user interface
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ICA:独立分量分析，基于MATLAB语言-ICA : Independent component analysis, based on MATLAB
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This a MATLAB code that performs
Independent Component Analysis
using a deflation approach.
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FASTICA - Fast Independent Component Analysis
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％ FastICA for Matlab 7.x and 6.x
％ Version 2.5, October 19 2005
％ Copyright (c) Hugo G鋠ert, Jarmo Hurri, Jaakko S鋜el� and Aapo Hyv鋜inen.
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％ FASTICA(mixedsig) estimates the independent
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用于盲信号分离的独立分量分析和主元分量分解以及独立分量分解的代码,For Blind Signal Separation of independent component analysis and principal component decomposition and independent component decomposition of the code
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改进的独立分量分析,在以往的独立分量分析中加入核函数，避免其缺陷，更好的分离信号。-Improvement of independent component analysis (ica), in the past the independent component analysis (ica) adding kernel function, avoid its defects, better separated signal.
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从盲声源信号的独立性出发!提出了一种新的盲声源混合信号分离方法:该方法基于信号联合概率的
分布统计!利用信号联合概率的方向导数熵最小获得最佳的旋转角度!最终实现盲信号分离:与快速独立分
量分析方法及神经网络方法相比!该方法不需要迭代计算:采用新的盲声源信号分离方法对轴承试验台的混
合声音信号进行识别!将电机和滚动轴承的声音分离出来!进而可以准确识别机械的故障-Blind sound source from the independence of the starting signal
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AMUSE，独立成分分析(ICA)算法之一，用于混合语音信号的盲分离-AMUSE, algorithm of independent component analysis, used in blind speech signal separation.
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关于独立成分分析的一个ppt,这个ppt对独立成分分析的介绍很全面,从原理,到算法实现都很清楚,希望对大家有所帮助!-With regard to independent component analysis, a ppt, the ppt presentation on independent component analysis is very comprehensive, from the principle, to the algorithm are well aware, we wan
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基于ICA的独立分量分析，目标函数是负熵，快速不动点算法-ICA-based independent component analysis, the objective function is negative entropy, fast fixed-point algorithm
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基于负熵的FASTICA的不动点算法，并行提取信号，独立分量分析-Based on negative entropy FASTICA the fixed point algorithm, parallel extract the signal, independent component analysis
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基于最大似然估计的独立分量分析算法，包括随机梯度算法，相对梯度算法，快速不动点算法3个程序-Based on maximum likelihood estimation of independent component analysis algorithms, including stochastic gradient algorithm, the relative gradient algorithm, fast fixed-point algorithm for three programs
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code for speaker recognition using independent component analysis
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一独立分量分析是在研究盲源分离过程中出现的一种全新的数据分析和信号处理方法
-An independent component analysis in the study of blind source separation is the process of the emergence of a new data analysis and signal processing methods
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基于峭度的快速独立分量分析的程序，内含三个源信号-Based on kurtosis fast independent component analysis program, containing three source signal
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互信息独立分量分析，克服传统独立分量分析的缺陷，很好的实现原信号波形恢复。-Mutual information independent component analysis, to overcome the defects of traditional independent component analysis, it is good to realize the original signal recovery.
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对三路信号进行分离，基于峭度和基于负熵的独立分量分析（ICA）-The three way signal separation, based on the kurtosis and independent component analysis (ICA) based on negative entropy
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Fetal electrocardiogram (fetal ECG) extraction is an interesting as well as a difficult problem in signal processing. This forms one important application of Independent Component Analysis (ICA) or Blind Source Separation (BSS),
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本程序是独立成分分析算法的主程序，在该程序中，通过调用子程序的方式实现了独立成分分析。-This procedure is the main independent component analysis algorithm, in this program by calling the subroutine way to achieve an independent component analysis.
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