Advanced Digital Signal Processing and Noise Reduction-猫眼课题宝

DOI：

10.1002/9780470740156

发表时间：

2006-01

期刊：

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通讯作者：

中科院分区：

其他

文献类型：

作者：

S. Vaseghi

目录符号缩写 1 引言 1.1 信号、噪声和信息 1.2 信号处理方法 1.3 数字信号处理的应用 1.4 采样和量化综述 1.5 摘要参考书目 2 噪声和失真 2.1 引言 2.2 白噪声 2.3 有色噪声粉红噪声和棕色噪声 2.4 脉冲和喀哒噪声 2.5 脉冲和喀哒声点击噪声 2.6 热噪声 2.7 散粒噪声 2.8 闪烁 (I/f) 噪声 2.9 突发噪声 2.10 电磁（无线电）噪声 2.11 通道失真 2.12 回波和多径反射 2.13 建模噪声 2.14 参考书目摘要 3 信息论和概率模型 3.1 简介：概率和信息模型 3.2 随机过程 3.3 概率模型 3.4 信息模型 3.5 平稳和非平稳过程 3.6 过程的期望值 3.7 一些有用的随机过程类别 3.8 随机过程的转换 3.9 搜索引擎：引文排名 3.10 参考书目摘要 4 巴耶斯推理 4.1 贝叶斯估计理论：基础定义 4.2 贝叶斯估计 4.3 估计最大化方法 4.4 最小估计方差上的克拉默-拉奥界限 4.5 高斯混合模型的设计 4.6 贝叶斯分类 4.7 随机过程空间建模 4.8 参考书目摘要 5 隐马尔可夫模型 5.1 非平稳过程的统计模型5.2 隐马尔可夫模型 5.3 训练隐马尔可夫模型 5.4 使用隐马尔可夫模型解码信号 5.5 DNA 和蛋白质序列建模中的 HMM 5.6 用于语音和噪声建模的 HMM 5.7 参考文献摘要 6 最小二乘误差维纳-柯尔莫哥洛夫滤波器 6.1 最小二乘误差估计：维纳-柯尔莫哥洛夫滤波器6.2 维纳滤波器的块数据公式 6.3 将维纳滤波器解释为向量空间中的投影 6.4 最小均方误差信号的分析 6.5 频域中维纳滤波器的公式 6.6 维纳滤波器的一些应用 6.7 维纳滤波器的实现 6.8 参考书目摘要 7 自适应滤波器、卡尔曼、RLS、LMS 7.1 简介 7.2 状态空间卡尔曼滤波器 7.3 扩展卡尔曼滤波器 7.4 无迹卡尔曼滤波器 7.5 样本自适应滤波器 7.6 递归最小二乘 (RLS) 自适应滤波器 7.7 最速下降法 7.8 LMS 滤波器 7.9 参考书目摘要 8 线性预测模型 8.1 线性预测编码 8.2 前向、后向和格预测器8.3 短期和长期线性预测器 8.4 预测器系数的 MAP 估计 8.5 共振峰跟踪 LP 模型 8.6 子带线性预测 8.7 使用线性预测模型进行信号恢复 8.8 参考文献摘要 9 特征值分析和主成分分析 9.1 简介 9.2 特征分析 9.3 主成分分析9.4 参考文献摘要 10 功率谱分析 10.1 功率谱和相关性 10.2 傅里叶级数：周期信号的表示 10.3.3 能谱密度和功率谱密度 10.3 傅里叶变换：非周期信号的表示 10.4 非参数功率谱估计 10.5 基于模型的功率频谱估计 10.6 基于子空间特征分析的高分辨率频谱估计 10.7 摘要参考书目 11. 插值 - 丢失样本的替换 11.1 简介 11.2 基于模型的插值 11.3 基于模型的插值 11.4 摘要参考书目 12 通过频谱幅度估计增强信号 12.1 简介12.2 噪声信号的频谱表示 12.3 噪声信号频谱的向量表示 12.4 频谱减法 12.5 贝叶斯 MMSE 频谱幅度估计 12.6 信噪比估计 12.7 在语音恢复和识别中的应用 12.8 参考书目摘要 13 脉冲噪声：建模、检测和去除 13.1 脉冲噪声 13.2 脉冲噪声的自相关和功率谱 13.3 脉冲噪声的概率模型 13.4 脉冲污染、信号与脉冲噪声比 13.5 中值滤波器 13.6 使用线性预测模型去除脉冲噪声 13.7 鲁棒参数估计 13.8 存档留声机记录的恢复 13.9 参考书目摘要 14 瞬态噪声脉冲 14.1 瞬态噪声波形 14.2 瞬态噪声脉冲模型 14.3 噪声脉冲检测 14.4 噪声脉冲失真的消除 14.5 参考书目摘要 15 回声消除 15.1 简介：声学和混合回声 15.2 回声返回时间：通信网络中延迟的根源 15.3 电话线混合回声 15.4 混合回声抑制 15.5 .i.自适应回声消除 15.6 声学 .i.Echo 15.7 .i.子带声学回声消除15.8.i.带线性预测预白化的回声消除 15.9 多输入多输出 (MIMO) 声学回声消除 15.10 摘要参考书目 16 通道均衡和盲解卷积 16.1 简介 16.2 使用通道输入功率谱的盲解卷积 16.3 基于线性预测模型的均衡 16.4 贝叶斯盲解卷积和均衡16.5 数字通信信道的盲均衡 16.6 基于高阶统计量的均衡 16.7 总结 16.8 参考文献 17 语音增强：降噪、带宽扩展和数据包替换 17.1 噪声中语音增强概述 17.2 单输入语音增强方法 17.3 语音带宽扩展 17.4丢失语音段 17.5 多输入语音增强方法 17.6 语音失真测量 17.7 摘要 17.8 参考书目 18 多输入多输出系统，独立分量分析 18.1 简介 18.2 MIMO 信号传播和混合模型 18.3 独立分量分析 18.4 摘要参考书目 19 移动通信中的信号处理19.1 蜂窝通信简介 19.2 移动系统中的通信信号处理 19.3 噪声、容量和频谱效率 19.4 移动通信中的多径和衰落 19.5 智能波束形成天线 19.6 参考书目索引

Contents Symbols Abbreviations 1 Introduction 1.1 Signals, Noise and Information 1.2 Signal Processing Methods 1.3 Applications of Digital Signal Processing 1.4 A Review of Sampling and Quantisation 1.5 Summary Bibliography 2 Noise and Distortion 2.1 Introduction 2.2 White Noise 2.3 Coloured Noise Pink Noise and Brown Noise 2.4 Impulsive and Click Noise 2.5 Impulsive and Click Noise 2.6 Thermal Noise 2.7 Shot Noise 2.8 Flicker (I/f) Noise 2.9 Burst Noise 2.10 Electromagnetic (Radio) Noise 2.11 Channel Distortions 2.12 Echo and Multi-path Reflections 2.13 Modelling Noise 2.14 Summary Bibliography 3 Information Theory and Probability Models 3.1 Introduction: Probability and Information Models 3.2 Random Processes 3.3 Probability Models 3.4 Information Models 3.5 Stationary and Non-stationary Processes 3.6 Expected Values of a Process 3.7 Some Useful Classes of Random Processes 3.8 Transformation of a Random Process 3.9 Search Engines: Citation Ranking 3.10 Summary Bibliography 4 Baseyian Inference 4.1 Bayesian Estimation Theory: Basic Definitions 4.2 Bayesian Estimation 4.3 The Estimate-Maximise Method 4.4 Cramer-Rao Bound on the Minimum Estimator Variance 4.5 Design of Gaussian Mixture Models 4.6 Bayesian Classification 4.7 Modeling the Space of a Random Process 4.8 Summary Bibliography 5 Hidden Markov Models 5.1 Statistical Models for Non-Stationary Processes 5.2 Hidden Markov Models 5.3 Training Hidden Markov Models 5.4 Decoding of Signals Using Hidden Markov Models 5.5 HMM In DNA and Protein Sequence Modelling 5.6 HMMs for Modelling Speech and Noise 5.7 Summary Bibliography 6 Least Square Error Wiener-Kolmogorov Filters 6.1 Least Square Error Estimation: Wiener-Kolmogorov Filter 6.2 Block-Data Formulation of the Wiener Filter 6.3 Interpretation of Wiener Filters as Projection in Vector Space 6.4 Analysis of the Least Mean Square Error Signal 6.5 Formulation of Wiener Filters in the Frequency Domain 6.6 Some Applications of Wiener Filters 6.7 Implementation of Wiener Filters 6.8 Summary Bibliography 7 Adaptive Filters, Kalman, RLS, LMS 7.1 Introduction 7.2 State-Space Kalman Filter 7.3 Extended Kalman Filter 7.4 Unscented Kalman Filter 7.5 Sample-Adaptive Filters 7.6 Recursive Least Square(RLS) Adaptive Filters 7.7 The Steepest-Descent Method 7.8 The LMS Filter 7.9 Summary Bibliography 8 Linear Prediction Models 8.1 Linear Prediction Coding 8.2 Forward, Backward and Lattice Predictors 8.3 Short-term and Long-Term Linear Predictors 8.4 MAP Estimation of Predictor Coefficients 8.5 Formant-Tracking LP Models 8.6 Sub-Band Linear Prediction 8.7 .i.Signal Restoration Using Linear Prediction Models 8.8 Summary Bibliography 9 Eigenvalue Analysis and Principal Component Analysis 9.1 Introduction 9.2 Eigen Analysis 9.3 Principal Component Analysis 9.4 Summary Bibliography 10 Power Spectrum Analysis 10.1 Power Spectrum and Correlation 10.2 Fourier Series: Representation of Periodic Signals 10.3.3 Energy-Spectral Density and Power-Spectral Density 10.3 Fourier Transform: Representation of Aperiodic Signals 10.4 Non-Parametric Power Spectrum Estimation 10.5 Model-Based Power Spectral Estimation 10.6 High Resolution Spectral Estimation Based on Subspace Eigen-Analysis 10.7 Summary Bibliography 11. Interpolation - Replacement of Lost Samples 11.1 Introduction 11.2 Model-Based Interpolation 11.3 Model-Based Interpolation 11.4 Summary Bibliography 12 Signal Enhancement via Spectral Amplitude Estimation 12.1Introduction 12.2 Spectral Representation of Noisy Signals 12.3 Vector Representation of Spectrum of Noisy Signals 12.4 Spectral Subtraction 12.5 Bayesian MMSE Spectral Amplitude Estimation 12.6 Estimation of Signal to Noise Ratios 12.7 Application to Speech Restoration and Recognition 12.8 Summary Bibliography 13 Impulsive Noise: Modelling, Detection and Removal 13.1 Impulsive Noise 13.2 Autocorrelation and Power Spectrum of Impulsive Noise 13.3 Probability Models for Impulsive Noise 13.4 Impulse contamination, Signal to Impulsive Noise Ratio 13.5 Median Filters 13.6 Impulsive Noise Removal Using Linear Prediction Models 13.7 Robust Parameter Estimation 13.8 Restoration of Archived Gramophone Records 13.9 Summary Bibliography 14 Transient Noise Pulses 14.1 Transient Noise Waveforms 14.2 Transient Noise Pulse Models 14.3 Detection of Noise Pulses 14.4 Removal of Noise Pulse Distortions 14.5 Summary Bibliography 15 Echo Cancellation 15.1 Introduction: Acoustic and Hybrid.i.Hybrid Echoes 15.2 Echo Return Time: The Sources of Delay in Communication Networks 15.3 Telephone Line Hybrid Echo 15.4 Hybrid Echo Suppression 15.5 .i.Adaptive Echo Cancellation 15.6 Acoustic .i.Echo 15.7 .i.Sub-band Acoustic Echo Cancellation 15.8 .i. Echo Cancellation with Linear Prediction Pre-whitening 15.9 Multiple-Input Multiple-Output (MIMO) Acoustic Echo Cancellation 15.10 Summary Bibliography 16 Channel Equalisation and Blind Deconvolution 16.1 Introduction 16.2 Blind-Deconvolution Using Channel Input Power Spectrum 16.3 Equalisation Based on Linear Prediction Models 16.4 Bayesian Blind Deconvolution and Equalisation 16.5 Blind Equalisation for Digital Communication Channels 16.6 Equalisation Based on Higher-Order Statistics 16.7 Summary 16.8 Bibliography 17 Speech Enhancement: Noise Reduction, Bandwidth Extension and Packet Replacement 17.1 An Overview of Speech Enhancement in Noise 17.2 Single-Input Speech Enhancement Methods 17.3 Speech Bandwidth Extension 17.4 Interpolation of Lost Speech Segments 17.5 Multiple-Input Speech Enhancement Methods 17.6 Speech Distortion Measurements 17.7 Summary 17.8 Bibliography 18 Multiple-Input Multiple-Output Systems, Independent Component Analysis 18.1 Introduction 18.2 MIMO Signal Propagation and Mixing Models 18.3 Independent Component Analysis 18.4 Summary Bibliography 19 Signal Processing in Mobile Communication 19.1 Introduction to Cellular Communication 19.2 Communication Signal Processing in Mobile Systems 19.3 Noise, Capacity and Spectral Efficiency 19.4 Multi-path and Fading in Mobile Communication 19.5 Smart Beam-forming Antennas 19.6 Summary Bibliography Index

Advanced Digital Signal Processing and Noise Reduction

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