基于EMD和ICA的单通道语音盲源分离算法

针对单通道语音信号盲分离的问题,结合盲源分离和经验模式分解的优点．提出了一种基于经验模式分解的单通道语音信号源数估计和盲源分离方法。对语音混合信号进行经验模式分解,利用贝叶斯算法估计语音源数目,根据源信号数目重组多通道语音混合信号,并采用独立分量分析实现语音信号的盲分离。仿真实验表明,使用此法能有效地估计通道语音信号源数和分离盲源。     

盲源分离中的非高斯性极大准则

盲源分离是试图从给定的一组混合观察数据中恢复未知的独立信号源。介绍了盲源分离常用的独立性度量准则之一——非高斯性极大准则，并阐明了其在盲源分离中的应用原理。     

源信号数目大于观察信号数目情况下的盲源分离

独立分量分析(ICA)作为一种有效的盲源分离技术(BSS）是信号处理领域的热点。传统的独立分量分析都要求观察信号数目大于或者等于源信号数目，然而对于脑电图(EEG)等的一些信号处理中存在的源信号数目大于观察信号数目的情况，传统的独立分量分析算法不能有效分离。该文针对源信号数目大于观察信号数目的情况，在传统的独立分量分析技术的基础上，给出了一个新的学习算法，并将新算法与传统的独立分量算法进行了比较。实验仿真结果证明该算法在给定2个混合信号的情况下能够较好地分离3个未知语音信号源，成功实现了源信号数目大于观察信号数目情况下的盲源分离。     

基于复数FastICA算法的短波同频信号盲源分离

盲源分离是一种经典信号处理方法,可广泛应用于通信信号、阵列处理的混合信号有效分离。将一种基于独立分量分析的复数信号盲源分离算法应用于实际的短波信号分离中,能有效地实现对各种信号组合的分离。     

独立分量分析盲信号分离方法研究

盲源分离是指从多个相互独立的源信号的混合信号中分离出源信号来。独立分量分析法是盲源分离的一种新方法,由于其在语音信号处理、阵列信号处理、生物医学信号处理、移动通信及图象处理等领域的应用前景,越来越引起人们的关注,成为研究的热点。介绍一种基于四阶累积量的非高斯性最大化的ICA算法解决盲源分离的问题,并给出了该算法分离通信信号的计算机仿真结果,验证了算法的有效性。     

一种任意信号源盲分离的高效算法

提出了信号源盲分离的DBBSS算法.利用随机变量概率密度函数非参数估计的核函数法,对混合信号的概率密度函数及其导数进行估计,并由此估计信号的评价函数(score function).解决了现有信号源盲分离算法中,普遍存在的非线性函数只能凭经验选取,以及混合信号同时包含超高斯信号和亚高斯信号时,算法失效的问题.该方法非常简单,可以直接应用于所有以非线性函数代替评价函数的信号源盲分离算法.仿真结果验证了算法的有效性.     

非理想信道多用户数字信号的盲分离

在一个信道上传送多个用户信号,可以大大提高信道的容量,本文讨论了非理想信道多和户数字信号的盲分离问题,利用天线阵,接收可以看作是由Ｎ个独立信号源激励的非线传输混合系统的输出;由于信道存在的码间干扰,混合矩阵的元素不是常数,而是一个线性子系统,针对这一情况,本文提出了一个盲分离器结构,首先将接收信号进行盲分离,然后利用基于ＡＲ模型的盲均衡器消除每一路输出信号的码间干扰,从而实现多用户信号物分离,语文     

应用萤火虫算法的混沌信号盲源分离

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。     

基于WPT分层和独立子波函数的单路心音混合信号BSS新方法

提出了一种基于小波变换分层和独立子波函数的单路混合信号的盲源分离新方法。首先讨论了单路混合信号分离模型,以及如何利用WPT进行窄带分层和获取独立子波函数的技术;然后通过结合独立子波函数进入单路混合信号,使单路混合信号由一维向量转化成为多维向量,以实现其盲分离;最后通过心音信号的分离实验,验证了本方法的有效性和可行性。     

基于子空间分解的多通道盲解卷积算法

针对卷积混合信号,提出了一种新的多通道盲解卷积算法,该算法首先利用子空间分解方法,将信号卷积混合模型变换成线性混合模型,然后利用线性混合盲分离算法分离出源信号.该算法相对频域盲解卷积算法来说无需解决线性混合盲分离中存在的幅度和排列顺序的模糊性问题,而且该算法不要求信号独立同分布,只要求各源信号统计独立即可.因此,该算法可以直接在中频对观察信号进行处理.计算机仿真结果表明,该算法不仅能对不同频不同调制方式的通信信号进行盲解卷积,而且对同频同调制的通信信号,该算法同样有效.     

盲信号分离

阵列处理和数据分析的一个典型问题是从混合的观测数据向量中恢复不可观测的各个源信号.盲信号分离是解决这一问题的一门新技术,近几年吸引了信号处理学界和神经网络学界众多学者的研究兴趣.本文将以独立分量分析和非线性主分量分析为主要对象,综述盲信号分离技术的理论、方法及应用等方面的发展,并作有关展望.     

盲信号分离及盲信号抽取研究

盲信号分离技术是将混合信号中的源信号分离出来的一种功能强大的信号处理方法,已成为信号处理领域的研究热点。阐述了盲信号分离的发展现状,介绍了盲信号分离问题的数学模型,给出了盲源分离的基本思想。对盲信号分离算法进行了研究,阐述了盲信号分离几种典型算法的特点及性能,对与盲信号分离紧密相关的盲信号抽取算法进行了总结,并对盲信号分离的进一步研究进行了展望。     

扩展信息最大化盲源分离算法的研究

盲源分离技术是信号处理和神经网络领域近年来的一个热点研究课题,由于其能够从观测的混合信号中恢复出源信号,而对源信号和混合系统的先验知识要求很少,因此在语音信号处理、无线信号处理、生物医学信号处理、地震信号处理,以及图像增强等方面都具有非常重要的理论意义和实用价值。信息最大化盲源分离算法能够有效地分离语音信号的瞬时混合,但是不能分离超高斯信号(如语音信号)和亚高斯信号(如正弦信号)的混合。基于此,本文讨论了扩展信启、最大化盲源分离算法,通过仿真表明,该算法可以有效的对各种源信号的线性即时混合进行分离,实验证明了该算法的有效性。     

EMI-based classification of multiple closely spaced subsurface objects via independent component analysis

Previous work in subsurface object discrimination using electromagnetic induction data has shown that discrimination algorithms based on statistical signal processing techniques are effective for classifying data from objects that occur in isolation. However, for multiple closely spaced subsurface objects, the raw (unprocessed) measurement is a mixture of the responses from several objects and as such cannot be used directly to determine the identity of each of the individual objects. Thus, we propose to separate individual signatures from the mixture by posing the problem as a blind source separation (BSS) problem and effecting signature separation using independent component analysis. We propose to apply BSS to separate the mixed signatures and then follow the separation process with a Bayesian classifier. This approach is evaluated using both simulated data and data from unexploded ordnance items. The results show that this approach can be used to effectively classify multiple closely spaced objects.     

Extracting Epileptic Feature Spikes Using Independent Component Analysis

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.     

Harmonic Signals Retrieval Approach Based on Blind Source Separation

A frequently encountered problem in signal processing is harmonic retrieval in additive colored Gaussian or non-Gaussian noise, especially when the frequencies of the harmonic signals are very close in space. The purpose of this paper is to develop an efficient Blind Source Separation (BSS) algorithm from linear mixtures of source signals, which enables to separate harmonic source signals using only one observed channel signal even if the frequencies of the harmonic signals are closely spaced. First, we establish the BSS based harmonic retrieval model in additive noise by using the only one observed channel, and analyze the fundamental principle by utilizing BSS method to retrieve harmonics. Then, we propose a BSS-based approach to the harmonic retrieval by resorting the concept of W-disjoint orthogonality in the over-complete BSS situation, and as a result, we get the separation algorithm using only one channel mixed signals. Simulation results show that the proposed separation algorithm-BSS-HR is able to separate the harmonic source signals.     

Sparsity and morphological diversity in blind source separation.

Over the last few years, the development of multichannel sensors motivated interest in methods for the coherent processing of multivariate data. Some specific issues have already been addressed as testified by the wide literature on the so-caIled blind source separation (BSS) problem. In this context, as clearly emphasized by previous work, it is fundamental that the sources to be retrieved present some quantitatively measurable diversity. Recently, sparsity and morphological diversity have emergedas a novel and effective source of diversity for BSS. Here, we give some new and essential insights into the use of sparsity in source separation, and we outline the essential role of morphological diversity as being a source of diversity or contrast between the sources. This paper introduces a new BSS method coined generalized morphological component analysis (GMCA) that takes advantages of both morphological diversity and sparsity, using recent sparse overcomplete or redundant signal representations. GMCA is a fast and efficient BSS method. We present arguments and a discussion supporting the convergence of the GMCA algorithm. Numerical results in multivariate image and signal processing are given illustrating the good performance of GMCA and its robustness to noise.     

Adaptive blind source separation for virtually any sourceprobability density function

Blind source separation (BSS) aims to recover a set of statistically independent source signals from a set of linear mixtures of the same sources. In the noiseless real-mixture two-source two-sensor scenario, once the observations are whitened (decorrelated and normalized), only a Givens rotation matrix remains to be identified in order to achieve the source separation. In this paper an adaptive estimator of the angle that characterizes such a rotation is derived. It is shown to converge to a stable valid separation solution with the only condition that the sum of source kurtosis be distinct from zero. An asymptotic performance analysis is carried out, resulting in a closed-form expression for the asymptotic probability density function of the proposed estimator. It is shown how the estimator can be incorporated into a complete adaptive source separation system by combining it with an adaptive prewhitening strategy and how it can be useful in a general BSS scenario of more than two signals by means of a pairwise approach. A variety of simulations assess the accuracy of the asymptotic results, display the properties of the estimator (such as its robust fast convergence), and compare this on-line BSS implementation with other adaptive BSS procedures     

盲信号分离在数字无线通信中的一种应用

本文提出了基于神经网络的盲信号分离在数字无线通信中的一种应用。利用本文中的天线阵,接收到的信号可以看作是由Ｎ个独立的信号源所激励的线性混合系统的输出,应用基于神经网络的鲁棒性很好的盲分离算法＾〖７〗,实现多用户信号的分离。我们还就其在数字无线通讯中的应用进行了计算机模拟,模拟结果显示分离效果是令人满意的。