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     

盲信号分离的现状和展望

盲信号分离是近几年才发展起来，用于解决从混合观测数据中分离源信号的一门新技术，已在许多领域获得了广泛应用。本文介绍了盲分离的主要理论和两大类实现方法——独立分量分析和非线性主分量分析，并在此基础上介绍了实现盲信号分离的不同算法、在非线性混合情况下的算法以及盲信号分离将来的发展方向。     

Temporal BYY learning for state space approach, hidden Markovmodel, and blind source separation

The temporal Bayesian Yang-Yang (TBYY) learning has been presented for signal modeling in a general state space approach, which provides not only a unified point of view on the Kalman filter, hidden Markov model (HMM), independent component analysis (ICA), and blind source separation (BSS) with extensions, but also further advances on these studies, including a higher order HMM, independent HMM for binary BSS, temporal ICA (TICA), and temporal factor analysis for real BSS without and with noise. Adaptive algorithms are developed for implementation and criteria are provided for selecting an appropriate number of states or sources. Moreover, theorems are given on the conditions for source separation by linear and nonlinear TICA. Particularly, it has been shown that not only non-Gaussian but also Gaussian sources can also be separated by TICA via exploring temporal dependence. Experiments are also demonstrated     

NGOSS方法论初探

本文介绍了方法论在OSS/BSS建设中的重要性,说明了抽象方法、商务过程和构件实现的分离、通用性和构件化方法在NGOSS中的应用,以及这些方法对于系统建设和实现的实际意义.这些意义包括:抽象方法的通用性、商务过程和构件实现的分离带来的系统灵活性、通用方法在电子商务方面的必要性以及构件系统在系统演化和系统集成方面的意义等.     

非线性盲源分离的原理及算法综述

本文主要阐述了非线性盲源分离(BSS)/独立成分分析(ICA)模型的基本数学原理、分离算法、算法性能及其应用。首先对线性和非线性BSS/ICA的数学模型作了介绍,重点介绍了非线性BSS/ICA解的不确定性,然后在此基础上对近十年来出现的各种非线性BSS/ICA算法进行简单综述,着重分析了一类可解且应用比较广泛的非线性BSS/ICA模型-后非线性BSS/ICA模型及其分离算法。最后对非线性BSS/ICA存在的问题和发展趋势进行了总结。     

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。     

盲分离与ASLC 抗干扰性能对比分析

针对盲分离和自适应旁瓣相消器(ASLC)两种抗干扰技术在实际中如何选择应用的问题,对盲分离和ASLC 的抗干扰性能进行了对比分析。在对盲分离和ASLC 抗干扰原理及信号处理技术研究的基础上,在多种场景及不同信噪比情况下对两种技术的抗干扰性能进行了仿真对比分析。通过分析,给出了不同干扰情况下抗干扰方法的选择依据,即ASLC 对于旁瓣干扰抑制更加有效,而主瓣干扰抑制方面盲分离技术更有优势。     

Noninvasive fetal electrocardiogram extraction: blind separation versus adaptive noise cancellation

The problem of the fetal electrocardiogram (FECG) extraction from maternal skin electrode measurements can be modeled from the perspective of blind source separation (BSS). Since no comparison between BSS techniques and other signal processing methods has been made, we compare a BSS procedure based on higher-order statistics and Widrow's multireference adaptive noise cancelling approach. As a best-case scenario for this latter method, optimal Wiener-Hopf solutions are considered. Both procedures are applied to real multichannel ECG recordings obtained from a pregnant woman. The experimental outcomes demonstrate the more robust performance of the blind technique and, in turn, verify the validity of the BSS model in this important biomedical application.     

基于谐波提取的欠定语音盲分离方法

现有的欠定语音信号盲分离算法往往不能同时兼顾分离性能及效率。针对此问题,本文提出一种基于谐波提取的欠定盲分离方法。首先,利用频谱校正从混合信号的短时傅立叶变换中提取谐波参数,其次利用相位一致性准则甄别这些参数的单源属性,进而用自适应K-均值方法对单源模式做聚类而获得源数估计和混合矩阵估计,最后再用子空间投影法恢复源信号。其中谐波提取和单源参数筛选可保证低复杂度地精确估计出混合矩阵。仿真实验表明,相比于原始子空间投影算法,本文方法可获得更高的信号恢复质量,且在谐波相关领域也具有潜在应用价值。      

Blind separation of independent sources for virtually any sourceprobability density function

The blind source separation (BSS) problem consists of the recovery of a set of statistically independent source signals from a set of measurements that are mixtures of the sources when nothing is known about the sources and the mixture structure. In the BSS scenario, of two noiseless real-valued instantaneous linear mixtures of two sources, an approximate maximum-likelihood (ML) approach has been suggested in the literature, which is only valid under certain constraints on the probability density function (pdf) of the sources. In the present paper, the expression for this ML estimator is reviewed and generalized to include virtually any source distribution. An intuitive geometrical interpretation of the new estimator is also given in terms of the scatter plots of the signals involved. An asymptotic performance analysis is then carried out, yielding a closed-form expression for the estimator asymptotic pdf. Simulations illustrate the behavior of the suggested estimator and show the accuracy of the asymptotic analysis. In addition, an extension of the method to the general BSS scenario of more than two sources and two sensors is successfully implemented     

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.     

一种源信号盲分离有效算法

本文研究接收信号维数大于源信号维数的盲分离，提出了一种基于广义特征函数的信号盲分离新方法，该方法提高了信号分离的精度，减少了计算量。文中就方法进行了理论推导，并给出了计算机仿真结果，仿真结果表明理论分析是正确的。     

基于两步法稀疏分量分析的欠定盲源分离

分析了解决欠定盲源分离问题的稀疏分量分析方法。首先讨论了数据矩阵稀疏表示（分解）的方法,其次重点讨论了基于稀疏因式分解方法的盲源分离。该盲源分离技术分两步．一步是估计混合矩阵,第二步是估计源矩阵。如源信号是高度稀疏的,盲分离可直接在时域内实现。否则．对观测的混合矩阵运用小波包变换预处理后才能进行。仿真结果证明了理论分析的正确性。     

基于GRD-Hough变换的多高斯源盲分离算法

盲源分离有一个重要假设:源信号最多只含一个高斯信号。否则,基于统计量的盲分离算法性能会恶化。本文从广义矩形分布出发,通过把时域中的一维信号映射到二维的时-频表示来提供信号的频谱内容随时间变化的信息,并对时频谱进行Hough变换处理,利用不同高斯源的时频分布差异性,避开统计量提出了一种能分离多个高斯源的盲分离算法,扩展了盲源分离的应用领域。     

基于改进人工蜂群算法的盲源分离方法

 针对现有盲源分离方法大多存在收敛速度慢、分离精度低的问题,提出一种基于改进人工蜂群(Artificial Bee Colony,ABC)算法的盲信号分离方法.在ABC的邻域搜索公式中自适应调整步长,并加入全局最优解指导项,增强局部趋化性搜索能力.改进的ABC算法保持了ABC全局搜索和局部搜索之间的平衡,使ABC算法可以达到更好的寻优效果,从而提高盲源分离算法的分离精度和稳定性.实验结果表明,提出的改进盲源分离算法可以有效地分离线性瞬时混合信号.与其它算法相比,该算法具有更优异的分离性能,并具有更快的收敛速度.     

Fast Approximate Joint Diagonalization Incorporating Weight Matrices

We propose a new low-complexity approximate joint diagonalization (AJD) algorithm, which incorporates nontrivial block-diagonal weight matrices into a weighted least-squares (WLS) AJD criterion. Often in blind source separation (BSS), when the sources are nearly separated, the optimal weight matrix for WLS-based AJD takes a (nearly) block-diagonal form. Based on this observation, we show how the new algorithm can be utilized in an iteratively reweighted separation scheme, thereby giving rise to fast implementation of asymptotically optimal BSS algorithms in various scenarios. In particular, we consider three specific (yet common) scenarios, involving stationary or block-stationary Gaussian sources, for which the optimal weight matrices can be readily estimated from the sample covariance matrices (which are also the target-matrices for the AJD). Comparative simulation results demonstrate the advantages in both speed and accuracy, as well as compliance with the theoretically predicted asymptotic optimality of the resulting BSS algorithms based on the weighted AJD, both on large scale problems with matrices of the size 100$,times,$100.      

语音信号识别基于盲源信号分离的实现

为了识别两路频谱混叠语音信号,多采用盲信号分离的方法。但是该方法在工程实践中实现较困难。因此给出了一种利用盲源信号分离的原理及特点的实现方法,具体说明了用FastICA算法在ADSP_BF533平台上实现盲源信号分离时的具体流程。该设计方案所需时间短,效率高,而且占用内存较少。     

结合ICA和复数神经网络的二麦阵列盲源分离技术

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。      

Blind signal separation of linear mixture using trilinear decomposition

This paper introduces a new source separation technique exploiting the time coherence of the source signals. The proposed approach relies only on stationary second order statistics. Blind Signal Separation (BSS) method using trilinear decomposition is proposed in this paper. Simulation results reveal that our proposed algorithm has the better blind signal separation performance than joint di-agonalization method. Our proposed algorithm does not require whitening processing. Moreover, our proposed algorithm works well in the underdetermined condition, where the number of sources exceeds than the number of sensors.     

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.