语音信号的盲分离分析

目录

摘要.................................................................... I ABSTRACT ............................................................... II 第一章前言.. (2)

1.1语音特性分析 (2)

1.2语音信号的基本特征 (2)

1.3语音信号处理的理论基础 (2)

第二章盲分离的基本概念 (2)

2.1盲分离的数学模型 (2)

2.2盲源分离的基本方法 (2)

2.3盲分离的目标准则 (2)

2.4盲分离的研究领域 (2)

2.5盲分离的研究内容 (2)

第三章独立分量分析的基本算法 (2)

3.1ICA的线性模型 (2)

3.2ICA研究中的主要问题及限制条件 (2)

3.3ICA的基本算法 (2)

3.4F AST ICA算法原理 (2)

第四章语音信号盲分离仿真及分析 (2)

4.1ICA算法实现 (2)

4.2频谱分析 (2)

第五章总结 (2)

参考文献 (2)

摘要

盲源分离（BSS）是一种多维信号处理方法，它指在未知源信号以及混合模型也未知的情况下，仅从观测信号中恢复出源信号各个独立分量的过程。盲源分离已近成为现代信号处理领域研究的热点问题，在通信、语音处理、图像处理等领域具有非常重要的理论意义和广泛的应用价值。本文主要内容如下：

首先，介绍了语音信号的产生机理，特性，基本特征及语音信号处理的理论基础，为后文语音信号盲分离奠定了基础。

其次，从盲源分离的理论出发，研究了盲分离的数学模型以及基本方法，并对盲分离的目标准则、研究领域以及研究内容进行了探讨。

然后，引出了独立分量分析（ICA），并对其的概念以及相关的知识进行了研究，探讨了ICA研究中的主要问题，列出了ICA的3种基本算法：信息极大化、负熵最大化和最大似然估计法。

最后，用FastICA对三路语音信号进行了盲分离的仿真并求出了混合矩阵和分解矩阵，再接着进行了频谱，幅度，相位的分析，找出了FastICA的特点。

关键词：盲源分离；独立分量分析；频谱分析

Abstract

Blind source separation (BSS) is a multidimensional signal processing method, it refers to the unknown source signal and mixed model also unknown cases, only from observation signal in recovering the source signal each independent component of the process. Blind source separation has nearly become modern signal processing to the research of problems, in communication, speech processing, image processing area is very important theoretical significance and broad application value. This paper mainly content as follows: First of all, introduced the speech signal generation mechanism, characteristics, basic characteristics and the speech signal processing theory foundation for the blind source separation after the speech signal to lay the foundation.

Second, the blind source separation from the theory, the mathematical model of the blind source separation and basic methods, and separation goal standards, research field and the research content are discussed.

Then, leads to a independent component analysis (ICA), and the concept and the related knowledge, this paper analyses the main problems in the study of ICA, lists the three basic ICA algorithm: information maximization, negative entropy maximization and maximum likelihood estimate.

Finally, by the use of FastICA three road voice signal the separation of the simulation and get the mixing matrix and decomposing matrix, and then the spectrum, amplitude, phase analysis, find out the FastICA characteristic.

Key words: the blind source separation; Independent component analysis; Spectrum analysis