【作者】 王卫华；

【导师】 黄凤岗；

【作者基本信息】 哈尔滨工程大学 ， 计算机应用技术， 2009， 博士

【摘要】 盲源分离是近年来在信号处理领域中出现的一个热点课题。它是指在未知源信号数目、位置和混合过程等先验信息的情况下,仅根据一组传感器信号来估计原始信号。盲源分离是数据分析及信号处理强有力的工具,在许多领域都有很好的应用,如生物医学信号处理、数据挖掘、语音信号处理、模式识别及无线通信等。二十几年来,由于广泛的应用和有效的数据处理能力,盲源分离算法吸引了大量学者的研究,并取得了很大进展。本文从语音信号的分离入手,对盲源分离算法进行了深入研究。论文首先对盲源分离算法的发展历史和研究现状进行了概括,并介绍了其在几个重要领域中的应用,接着介绍了盲源分离问题的几类主要算法。本文对盲源分离的理论和算法进行了探讨,重点研究了欠定情况下的盲分离问题。主要研究内容有以下几个方面：研究了频域盲分离算法中的排序不确定性问题。利用同一个语音信号相邻频点相关性大于两个不同语音信号相邻频点相关性的特点,提出了一个基于相邻频点幅度相关性的排序不确定性消除算法,使得在每个频点进行独立分量分析和排序同步进行。研究了欠定情况下的时频域盲分离算法。利用双耳的听觉机理,提出了个基于听觉场景分析的盲分离算法,通过对听觉特征耳间时间差和耳间强度差的聚类,完成对多个源信号混合而成的两个传感器信号的分离。另外,本文还提出了一种用于卷积混合的语音盲分离算法。通过在混合信号时频域的聚类,实现了欠定情况下的卷积盲分离。研究了源信号在时频域不充分稀疏情况下的欠定盲分离问题。提出一个新的二阶段式欠定盲分离算法,利用网格和密度聚类的方法,可以更好地估计混合系数矩阵。在源信号恢复时,使用简化的方法求最小l1范数解,网格的使用有效地减少了计算量。提出了一种基于匹配追踪的稀疏源信号恢复算法。针对匹配追踪算法在盲源分离的稀疏源信号恢复问题中的具体应用,将匹配追踪算法加以改进,有效地提高了算法的性能。提出的算法在病态混合矩阵的情况下,可以减少匹配失败时所带来误差,具有良好的鲁棒性。更多还原

【Abstract】 Blind source separation (BSS) is a newly presented and lively area of signal processing domain in recent years, which estimates sources given mixed signals without prior knowledge such as sources number, location and mixing process. BSS is a powerful tool of data analysis and signal processing, and it has been applied in many areas such as biological and medicine signal processing, data mining, speech signal processing, pattern recognition, wireless communication and so on.Considering its wide application and ability of data processing, Researchers have paid a great deal of attention to BSS and made a great progress in the past twenty years. This thesis researches the separation algorithms of speech signals. Firstly, a brief introduction of the development history and current research status are summarized, and several applications of the BSS are given. Then, this thesis introduces several kinds of important algorithms of BSS.This thesis discusses the theories and algorithms of BSS, and especially researches the underdetermined blind source separation problem. The researches concentrate on the following topics:The blind separation problem of convolutive mixtures and delayed mixtures is discussed in this thesis. The separation when there are more sources than mixtures is researched emphatically. The main works of this thesis are as follows:The permutation indetermination problem in the frequency domain blind source separation is discussed. By utilizing the characteristic that amplitude correlation between neighbor bins of the same signal is better than that of different signals, an improved method based on the amplitude correlation between neighbor bins to eliminate the permutation indetermination is proposed. It is feasible to implement ICA algorithm and permutation method simultaneously.The underdetermined blind source separation algorithms in time-frequency domain are researched. A novel blind source separation algorithm based on computational auditory scene analysis (CASA) is proposed, which can separate several sources from two sensor signals by clustering to interaural time differences (ITD) and interaural intensity differences (IID). A speech blind separation algorithm for convolute mixture is proposed, which can separate convolved mixtures in underdetermined case by clustering in time-frequency domain of mixtures.The blind separation problem for sources that are sparse insufficiently is researched. A novel two-step underdetermined blind source separation algorithm is presented, which estimates mixing coefficient more efficiently using clustering algorithm based on grid and density, and it estimates the mixing matrix better. When recovering source signals, a simpler method is used to get l1, norm minimization solution.An algorithm of sparse sources recovery based on matching pursuit (MP) is proposed. Considering the application of MP in sparse sources recovery in blind source separation, this paper improves classical MP algorithm and has obtained a better performance. Proposed method works well even the mixing matrix is ill-conditioned by reduce the error when match failed.更多还原