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在源信号和传输信道未知情况下,只利用接收天线的观测数据抽取源信号,称为盲信号分离.盲信号分离不仅是信号处理界、而且也是神经网络界的研究热点课题,在无线数据通信、雷达、图像、语音、医学以及地震信号处理等领域都具有广阔的应用前景.采用自然梯度法和分阶段学习法。-The source signal and transmission channel is unknown circumstances, using only observational data extraction receiving a
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Source Code in Matlab implementing Source Separation as used in Bell and Sejnowski 1996.An information maximisation approach to blind separation and blind deconvolution.
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独立分量分析(ICA)以非高斯源信号为研究对象,在统计独立的假设下,对多路观测到的混合信号进行盲信号分离,已广泛应用于无线通信、生物信号提取、语音信号处理、图像处理和噪声抑制等领域。 -The independent component analysis (ICA) addresses non-Gaussian source signals under amusing independent each other, it performs blind separation for multi-c
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盲信号语音分离的程序,基于独立分量的分离程序,若输入两个源信号,则可以输出一组混合信号和分离后的信号-Blind signal speech separation procedure, based on independent component separation procedure, enter the two source signals, the output signal after a group of mixed-signal and separation
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这是实际环境中语音信号盲分离的最新程序源码代码,用于语音信号独立分量分分析ICA。解压后运行,输入录制的混合语音信号即可看到结果。 可直接使用。
-This is the program source code for blind separation of speech signals in the real environment for the voice signal of independent component analysis of the ICA. After decomp
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统计稀疏分解(SSDP)欠定盲分离MATLAB仿真程序。能在接收天线个数小于源信号个数时,仅仅利用观察信号恢复出源信号。-Statistics sparse decomposition (SSDP) underdetermined blind separation of MATLAB simulation program. In the number of receiving antennas is less than the number of source signals, using on
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file:mblms23.m-performs two by three source separation with multipath
multichannel blind equalization with two sources and three sonsors
by Russell H.Lambert 1995
Must use multichannel ISI as performance measure (instead of MSE, SIR)
beca
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实现盲源信号的分离,在一个信号源中提取出有用的信号。-Blind source signal separation, in a signal source to extract useful signals.
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实现盲源信号的分离,在一个信号源中提取出有用的信号。-Blind source signal separation, in a signal source to extract useful signals.
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该工具箱描述了基于蚁群算法和互信息进行非线性盲源信号分离-The toolkit is described based on ant colony algorithm and mutual information of nonlinear blind source signal separation
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利用JADE算法实现盲源信号的分离,JADE算法是盲源信号分离的常用算法-Using JADE algorithm for blind source signal separation, JADE algorithm is a commonly used algorithm of blind source signal separation
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常用的盲分离算法有二阶统计量方法、高阶累积量方法、信息最大化(
Infomax
)以及独
立成分分析(
ICA
)等。这些方法取得最佳性能的条件总是与源信号的概率密度函数假设有关,
一旦假设的概率密度与实际信号的密度函数相差甚远,分离性能将大大降低。本文提出采用
核函数密度估计的方法进行任意信号源的盲分离,并通过典型算例与几种盲分离算法进行了
性能比较,验证了方法的可行性。(The commonly used blind separation algorithms include
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主要实现源信号盲分离,注释比较清楚,能够运行,对于初学者帮助较大(The main source of blind separation of the signal, the note is relatively clear, able to run, for beginners to help larger)
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ica fasst algorthim signal processing audio separation blind source
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