Description: 这是利用特征值分解的盲源分离的源函数文件，可以直接调用-This is the eigenvalue decomposition of Blind Source Separation of source function, can be called directly Platform: |
Size: 1024 |
Author:陈勇 |
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Description: ICA算法的一个例子，适于初学者。其中采用实际的数据进行盲分离。-ICA an example of the algorithm, suitable for beginners. The actual use of these data for blind separation. Platform: |
Size: 53248 |
Author:马明 |
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Description: 盲分离算法，处理声音信号的分离，利用算法分离出语音信号，供语音识别使用-blind separation algorithm, the signal processing sound separation, the use of algorithms to isolate voice signal, for the use of speech recognition Platform: |
Size: 14336 |
Author:扬扬 |
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Description: 这个压缩包里包含4篇关于盲信号分离的英文文献-This bag contains 4 compression on the Blind Signal Separation of English literature Platform: |
Size: 738304 |
Author:关成 |
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Description: This is a HJ neural network for blind source separation. User could replace signals you interest in file s4.m. Running main file HJ_BSS.M, the mixed sources would be sparated. -This is a HJ neural network for blind source separation. User could replace signals you interest in file s4.m. Running main file HJ_BSS.M, the mixed sources would be sparated. Platform: |
Size: 1024 |
Author:劉立言 |
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Description: Blind Source Separation toolbox, This work has been partly funded by the European project BLIS (IST-1999-14190) Platform: |
Size: 15360 |
Author:张强 |
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Description: This file implements the Multi User Kurtosis Algorithm for Blind Source Separation (see [1] for more details about the method).
The zip file contains:
- the one_shot.m script file
- the MUK_algorithm.m function
The one_shot.m file illustrate the bahaviour of the algorithm for a simple MIMO communication using QPSK modulation. Just unzip the zip files in your computer and run the one_shot script. Platform: |
Size: 3072 |
Author:Alex |
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Description: 这是一些有关语音信号盲分离方面的外国文献，很有参考价值！该文献言简意赅，容易理解！-These are the blind separation of speech signals of foreign literature, a good reference! The literature concise, easy to understand! Platform: |
Size: 19408896 |
Author:高菲悦 |
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Description: 提出一种新的盲分离标准，基于这种标准构建横向反馈的线性前馈神经网路，并给出理论分析与实验结果-We presents a new necessary and sufficient condition for
the blind separation of sources having non-zero kurtosis,
from their linear mixtures. It is shown here that a new
blind separation criterion based on both odd (
)
and even (
) functions, presents desirable solutions,
provided that all source signals have negative kurtosis
(sub-Gaussian) or have positive kurtosis (super-Gaussian).
Based on this new separation criterion, a linear feedforward
network with lateral feedback connections is constructed.
Both theoretical and computer simulation results
are presented. Platform: |
Size: 559104 |
Author:筱美 |
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