盲分离问题的可分性理论

对盲信号分离的基本理论问题进行了探讨,给出了从混叠信号中分离出源信号的充要 条件,并回答了源信号能提取的数量问题.同时,还给出了基于该理论的盲分离算法,该方法适应 于病态混叠情形下的提取.且就几种不同的情况进行了计算机仿真.仿真结果表明了所提理论的 正确性及相应算法的有效性.     

超定条件下的盲信号提取算法

基于信号峭度理论,提出一种超定条件下的盲信号提取算法。该算法将混合矩阵辨识转化为一系列Givens矩阵辨识,从观察信号中一次提取出一个源信号。对于超定盲信号分离问题,待未知所有独立分量分离出后,余下分量可以看作是一个或多个独立分量的拷贝,是冗余信号。在算法运行结束后,所有源信号分离出,实现超定盲信号分离。该算法计算简单,收敛性好。计算机仿真试验验证了算法的有效性。     

一种多小波滤波器在基音周期提取中的应用

基音周期是嗓音信号的重要特征参数之一,它在病态嗓音医学临床检测中有广泛的应用。推导了一组双正交多小波滤波器,提出了多小波滤波器与自相关结合的基音周期检测方法,构造了包含汉语拼音6个元音的短语句,利用多小波滤波器从语音信号中提取低频信号,再使用自相关法检测语音信号基音周期。结果表明,该方法提取基音周期具有正确率较高、准确率较高和抗噪性较强的特点,在不同噪声环境下均优于自相关法、单小波与自相关法相结合的方法,尤其在较大噪声干扰下该方法具有明显的抗噪能力,不受语音信号非平稳特性的影响,可以有效地提取病态嗓音的基音周期。     

约束独立分量分析(CICA)算法在故障诊断中的应用

盲源分离(BSS)是利用源信号间的统计独立性,在源信号和混合通道都是未知的条件下,仅由观测信号分离出各源信号的过程,也称独立分量分析(ICA)。经典的ICA仅仅用到数据的统计信息特征,即统计独立性。然而,机械故障存在其他如频率特征等已知的先验知识,将主要利用这些先验信息进行故障诊断。提出一种带参考信号约束的ICA算法(CICA)进行盲源信号的分离,选取与待提取信号频率相同的脉冲信号作为参考信号,以均方误差作为相似性测度的方法进行了实验仿真,仿真实验表明CICA算法能够很好地分离出待提取信号。     

欠定条件下的盲分离算法

盲信号分离中当源信号个数大于观测信号个数，且源信号不是足够稀疏时，如果利用聚类算法进行分离，分离效果将会变差。为此提出一种在此欠定条件下新的盲信号分离算法。利用源信号的“稀疏性”估计混合矩阵，然后简化混合矩阵构造新的混合模型。由于源信号间具有的独立性，使得可以在新的混合模型中从观察信号的自相关函数中估计出源信号的频谱，从而达到分离出源信号的目的，且分离效果优于聚类算法。最后给出仿真试验实例，试验结果验证了算法的有效性。     

基于Bessel函数展开的ICA语音增强

将源信号的先验知识以参考信号的形式引入到独立分量分析(ICA)学习算法中,从混合信号中仅提取期望的源信号。依据语音信号传播机理和Bessel函数展开系数对语音信号的表征能力,给出基于Bessel函数展开的参考信号构建方法,从混合语音信号中提取出期望的语音信号。仿真和性能分析结果表明,该方法能在噪声干扰的情况下达到语音增强的目的。     

欠定情形下语音信号盲分离的新方法

提出了一种新的两步法来实现欠定情形下语音信号的盲分离。第一步,采用一种重构观测信号采样点搜索法来估计混合矩阵;第二步,提出了一种伪提取矢量的概念,通过伪提取矢量来提取取值占优的源信号的采样值来恢复源信号。在源信号的恢复过程中,还使用了经典的基于线性规划的欠定盲源分离方法。结果表明：该方法由于在信号的各采样点处无须优化,在源信号的分离过程中,分离速度要比基于线性规划的方法快数倍,且分离精度不低于基于线性规划的方法。仿真结果表明了该算法的良好性能。     

交流电机调速系统振动信号的盲源分离

介绍了盲源分离的基本概念以及基于负熵的盲源分离技术。以交流电机变频调速系统振动信号为研究对象,介绍了快速定点独立分量分析（Fast ICA）方法,并通过MATLAB软件编程,对系统的实测振动信号进行了分离。分离结果表明,该算法能够从复杂振动信号中有效地分离出大部分源信号,实现主要振动源信号的提取。     

混合矩阵为病态情况下的盲信号提取快速算法

在盲信号提取技术中,当混合矩阵是病态情况（混合矩阵奇异或欠定）时,根据混合矩阵秩的性质,提出了在该情况下的盲信号可提取性判据,并给出了一种可提取信号的快速提取算法。该算法中只使用了待提取信号与其他观测量之间的四阶累积量,而不需要计算各个观测量之间的四阶累积量,因此大大减少了算法的复杂度。先提取信号的误差没有对后提取的信号产生累积,因此提高了提取信号的精确度。该算法简单。仿真结果表明该算法有效,并且有很好的性能。     

特征基函数ICA在航空发动机故障诊断中的应用

针对航空发动机的振动信号,提出了特征基函数ICA提取和SVM相结合的航空发动机故障诊断方法。首先利用ICA从混合振动信号中提取源信号,再利用ICA进一步提取源信号的特征基函数,将特征基函数的频域特性——峰值频率和拐角频率作为特征样本数据用于支撑向量机进行模式识别。采用该方法对某航空涡扇发动机的振动信号进行了分析。分析结果表明,该方法比直接使用SVM的故摩诊断准确率高,证明了基函数提取特征的有效性。     

基于细菌觅食优化的盲信号提取算法*

提出了一种新的基于细菌觅食优化的盲信号提取算法。采用负熵作为信号提取的目标函数,利用球坐标变换原理将对提取向量的求解转换为对旋转角度的求解。使用改进的细菌觅食优化算法对目标函数进行优化求解,从而实现对源信号的盲提取。通过多次提取和去相关消源过程,可以实现对所有源信号的成功提取。对多路语音信号混合后的盲提取实验验证了所提出算法的有效性。     

基于偏度的低空目标声信号盲抽取算法

针对盲源分离信号顺序的不确定性以及基于峭度的盲抽取算法运算量大的问题,本文提出一种基于偏度的低空目标声信号盲抽取算法.该算法利用偏度的非对称性,将其作为信号非高斯性的度量,抽取出的信号可以按偏度绝对值的降序排列.与基于峭度的盲抽取算法相比,该算法大大减小了运算量,同时具有较好的分离效果,且对于噪声具有鲁棒性.声音信号及...     

An efficient semi-blind source extraction algorithm and its applications to biomedical signal extraction

In many applications, such as biomedical engineering, it is often required to extract a desired signal instead of all source signals. This can be achieved by blind source extraction (BSE) or semi-blind source extraction, which is a powerful technique emerging from the neural network field. In this paper, we propose an efficient semi-blind source extraction algorithm to extract a desired source signal as its first output signal by using a priori information about its kurtosis range. The algorithm is robust t...     

Extraction of specific signals with temporal structure

In this work we develop a very simple batch learning algorithm for semiblind extraction of a desired source signal with temporal structure from linear mixtures. Although we use the concept of sequential blind extraction of sources and independent component analysis, we do not carry out the extraction in a completely blind manner; neither do we assume that sources are statistically independent. In fact, we show that the a priori information about the autocorrelation function of primary sources can be used to extract the desired signals (sources of interest) from their linear mixtures. Extensive computer simulations and real data application experiments confirm the validity and high performance of the proposed algorithm.     

An algorithm for extracting fetal electrocardiogram

The extraction of fetal electrocardiogram (FECG) from the composite maternal ECG signal is discussed. This problem can be modelled from the perspective of blind source extraction. An important and primary work is done by Barros and Cichocki, who propose an FECG extraction method for the noisy-free mixing model. However, it is realistic to extract the FECG from noisy measurements. Therefore, we propose a new algorithm for the FECG extraction with additive noise. Theoretical analysis and simulation results confirm the validity of the proposed algorithm.     

Harmonic retrieval by period blind source extraction method: Model and algorithm

A frequently encountered problem in signal processing field is harmonic retrieval in additive colored Gaussian or non-Gaussian noise, especially when the frequencies of the harmonic signals are closely spaced in frequency domain. The purpose of this paper is to develop novel harmonic retrieval algorithm based on blind source extraction (BSE) method from linear mixtures of harmonic signals using only one observed channel signal. First, we establish the blind source separation (BSS) based harmonic retrieval model in additive noise using the only one observed channel, at the same time, the fundamental principle of BSE based harmonics retrieval algorithm is analyzed in detail. Then, based on the established harmonic BSS model, we propose a BSE approach to the harmonic retrieval using the concept of period BSE method, as a result, the harmonic retrieval algorithm using only one channel mixed signals is derived. Simulation results show that the proposed algorithm is able to separate the harmonic source signals and yield ideal performance.     

Mixing Audiovisual Speech Processing and Blind Source Separation for the Extraction of Speech Signals From Convolutive Mixtures

Looking at the speaker's face can be useful to better hear a speech signal in noisy environment and extract it from competing sources before identification. This suggests that the visual signals of speech (movements of visible articulators) could be used in speech enhancement or extraction systems. In this paper, we present a novel algorithm plugging audiovisual coherence of speech signals, estimated by statistical tools, on audio blind source separation (BSS) techniques. This algorithm is applied to the difficult and realistic case of convolutive mixtures. The algorithm mainly works in the frequency (transform) domain, where the convolutive mixture becomes an additive mixture for each frequency channel. Frequency by frequency separation is made by an audio BSS algorithm. The audio and visual informations are modeled by a newly proposed statistical model. This model is then used to solve the standard source permutation and scale factor ambiguities encountered for each frequency after the audio blind separation stage. The proposed method is shown to be efficient in the case of 2 times 2 convolutive mixtures and offers promising perspectives for extracting a particular speech source of interest from complex mixtures     

小波变换结合盲源分离的EEG情感识别

由于脑电信号独立源数目的不确定性以及其他噪声的干扰,使得采集的脑电信号各导信号之间产生串扰、源信号难以估计以及噪声混杂等问题,严重影响了对脑电信号的分析研究。将小波变换与盲源分离算法相结合,并对盲源分离算法中维格纳分布存在的交叉项干扰现象进行重排处理。主要思路是首先将每一导信号进行小波变换,提取出特征波β波,然后对这些β波信号进行基于重排光滑伪维格纳分布的盲源分离,分离出关联性极大的β波成分。实验结果表明,所用方法分离出了各导信号中关联性大的脑电信号成分,并在一定程度上解决了源信号难以估计等问题,使识别结果有明显的提升。