【作者】 高波；

【导师】 邱天爽；

【作者基本信息】 大连理工大学 ， 信号与信息处理， 2009， 硕士

【摘要】 盲源分离是信号处理领域的一个新的研究方向,同时也是一个引人瞩目的应用热点。牵涉到诸如信息理论、神经网络、统计信号处理、优化理论等领域。近年来,对于盲源分离的研究受到了国内外广泛重视,研究也越来越广泛和深入,理论和实际应用都得到了很大的发展。实际生活中,人们接受到的信号往往是不同信源产生的多路信号的混合。例如:鸡尾酒会效应,EP信号的提取等。所谓盲源分离就是在对信源和混合方式等先验知识特别少的情况下,仅由观测到的混合信号来推测或恢复源信号。本文对经典ICA算法进行了研究。经典的ICA方法,如FastICA、Infomax、JADE等,都是应用在观测信号的数目不小于源信号的数目的情形。通过仿真对其分离结果进行对比研究,对于观测信号数目不小于源信号数目的情形,FastICA、Infomax、JADE等算法都能很好的进行分离,其中FastICA算法具有收敛速度快,可以逐个提取各独立分量,计算简单等优点。对于观测信号的数目小于源信号的数目的情况,即盲源分离的欠定情况,是目前研究的热点和难点。本文对于欠定的盲源分离问题进行深入研究,对于源信号具有稀疏性的语音信号和不具有稀疏性的EP信号进行探索研究,并通过仿真分离提取出源信号。通过对时域不满足稀疏性的语音信号变换到变换域使其满足条件,估计出混合矩阵,然后利用最短路径法,分离得到源信号的估计。对于EP信号的单导单次提取,由于EP信号不具有稀疏性,不能使用传统的欠定盲源分离算法。本文利用PCA进行处理,然后结合EMD,得到源信号的初步估计,然后经算法推导,建立模型,实现EP信号单导单次提取。更多还原

【Abstract】 Blind source separation(BSS) is a new domain of signal processing, it is also a hot spotlight of application. It refers to information theory, neural network, statistic signal processing, optimization theory and so on. Recently, the research on blind source separation is paid attention to and explored deeply and widely home and abroad, and its theories and applications are developed widely.In reality, the signal we received is often a mixture of many signals generated by different sources. For example, cocktail party problem, EP signal extraction problem, and so on. Blind source separation is, with little prior knowledge about signal sources and mix mode, given only the observed signals, to estimate or recover original signals.In this paper classic ICA algorithms are researched. Classic ICA algorithms, such as FastICA、Infomax、JADE etc, are based on the assumption that the number of observed signals must be no less than that of original signals. Through comparison to the simulation experimental results for the situation that the number of observed signals is no less than that of original signals, FastICA、Infomax、JADE get good results. The advantages of FastICA are faster convergence, able to extract components individually, simple computing.The situation that the number of observed signals is less than that of original signals, namely underdetermined BSS, is a hotspot and difficult spot. This paper also explores underdetermined BSS, and does research on the speech signal satisfied sparseness and EP signal not satisfied sparseness, gets the estimated signals of source by simulation. The speech signals not satisfied sparseness in time domain is transformed to transformed domain so that we can get the estimation of sources. For the extraction of EP signal with single-trail in single channel, because EP signal is not satisfied sparseness, traditional underdetermined BSS algorithms can not be used. This paper extracts EP signal with single-trail in single channel through PCA processing, with EMD, gets the pre-estimated signals, through algorithm derivation and founding a model, gets the EP with single-trail in single channel.更多还原