具体描述
本书主要介绍基于互联网场景的交互式实时语音处理流程，内容涉及智能语音助手、智能音箱、音/视频会议等，具体包括实时语音信号处理、数字音效、网络传输编/解码和语音唤醒识别四部分。在阐述各部分的内容时，本书从基本概念和原理入手，将理论和实践相结合，并细致分析了极具商业价值的实例，以帮助读者了解相关算法在工程上是如何实现的。另外，为便于有兴趣的读者快速进行算法验证并将其改进和应用到实际的项目中，作者也开源了书中算法的源码。
好的，这是一本名为《实时语音处理实践指南》的图书简介，内容将详细阐述其涵盖的主题，但不包含您所提到的核心内容。 --- 图书名称： 实时语音处理实践指南 图书简介 深入探索现代计算领域的关键基石：数据结构、算法与系统设计 在当今由海量信息和复杂计算驱动的时代，构建高效、可靠的软件系统比以往任何时候都更为重要。本书《实时语音处理实践指南》旨在为读者提供一个全面而深入的视角，聚焦于软件工程的核心理论与实践，特别是那些支撑现代高性能计算和大规模数据处理的底层技术。本书的叙述脉络清晰，理论深度与工程实践紧密结合，旨在培养读者从底层逻辑到系统架构的全面技术视野。 第一部分：数据结构与底层原理 本书的开篇将系统地回顾和深化读者对核心数据结构的理解。我们不仅会讲解数组、链表、栈和队列这些基础结构，更会深入探讨它们的内存布局、时间与空间复杂度分析，以及在特定应用场景下的优化策略。 树形结构与图论基础： 重点解析二叉搜索树（BST）、平衡树（如AVL树与红黑树）的旋转与维护机制。随后，我们将全面覆盖图论的基础，包括图的表示法（邻接矩阵与邻接表）、遍历算法（DFS与BFS），以及最短路径算法（Dijkstra、Floyd-Warshall）。特别强调这些结构在解决优化问题和网络路由中的实际应用。 哈希与映射机制： 深入剖析哈希函数的构造原理、冲突解决策略（链地址法、开放寻址法），以及如何设计高效的布尔查找表和关联容器。内容将扩展至一致性哈希在分布式系统负载均衡中的应用。 内存管理与缓存一致性： 探讨操作系统如何管理内存（堆、栈、数据段），以及程序如何通过局部性原理优化数据访问。分析CPU缓存层级（L1, L2, L3）的工作机制，以及如何编写“缓存友好”的代码以最大化并行处理效率。 第二部分：高效算法设计与分析 本部分是全书的理论核心，致力于教授读者如何系统地分析问题、选择合适的算法范式，并进行严谨的性能评估。 分治法、动态规划与贪心策略： 详细阐述这三种核心算法思想的应用边界。通过大量的实例（如矩阵链乘法、最长公共子序列、背包问题），展示动态规划的状态转移方程的建立过程。对于贪心算法，强调证明其最优子结构的关键步骤。 排序与搜索的极限优化： 除了标准的快速排序和归并排序外，本书将分析内外部排序的适用场景，特别是如何利用外部存储进行大规模数据集的排序。深入探讨二分查找的变种及其在有序数据集中的应用。 计算复杂性理论入门： 介绍P、NP、NP-完全等概念，帮助读者理解哪些问题在理论上是“容易”解决的，哪些问题是“计算上不可行”的。重点讲解如何利用多项式时间验证解的正确性。 第三部分：现代系统架构与并发编程 在现代多核处理器和大规模并行计算的背景下，理解如何构建高吞吐量、低延迟的系统至关重要。本部分侧重于将底层数据结构和算法知识应用于构建健壮的软件系统。 并发模型与线程同步： 详细对比进程与线程的区别，深入研究互斥锁（Mutex）、信号量（Semaphore）、条件变量（Condition Variables）等同步原语的使用与陷阱。重点分析死锁的成因、检测与预防机制。 无锁编程（Lock-Free Programming）： 介绍基于原子操作（如CAS循环）的并发数据结构设计，例如无锁队列和无锁栈，并分析其在极端性能要求下的优势与复杂性。 分布式系统基础概念： 阐述CAP定理、一致性模型（顺序一致性、线性一致性等）的基本原理。讨论远程过程调用（RPC）的实现机制及其在构建跨服务通信中的作用。 第四部分：性能工程与代码优化 本书的最后部分聚焦于实践，教授读者如何测量、分析和改进代码的性能。 性能剖析工具的使用： 引导读者熟练运用系统级的性能分析工具（如`perf`, `gprof`等），识别程序中的性能瓶颈，是CPU受限还是I/O受限。 I/O 优化与异步编程： 探讨阻塞式I/O与非阻塞I/O（如`epoll`, `kqueue`）的差异。深入讲解事件驱动编程模型（Reactor模式）的设计与实现，以处理高并发的网络连接。 编译器的优化与底层代码生成： 简要介绍编译器如何进行循环展开、函数内联和指令调度，帮助开发者编写出更易于编译器进行优化的代码结构。 面向读者： 本书适合具有一定编程经验的软件工程师、计算机科学专业的学生，以及任何希望系统性提升其算法设计能力、理解底层系统原理，并致力于构建高性能、可扩展软件的专业人士。通过本书的学习，读者将能自信地驾驭复杂的数据挑战，并设计出更具工程价值的解决方案。
作者简介
葛世超，硕士，毕业于西安电子科技大学雷达国防重点实验室，先后任职于阿里巴巴和rokid，从事语音算法工作。
吕强，学士，吉林大学通信工程专业毕业，原微鲸电视系统软件音频专家。
钱思冲 武汉理工大学博士，2016年至2018年在rokid从事麦克风阵列信号研究，目前主要研究语音信号盲源分离。
张博伦，硕士研究生，毕业于中国海洋大学海底科学与探测技术教育部重点实验室。毕业后先后从事水声、音频信号处理等工作。
张硕，毕业于西安电子科技大学和法国高等电力学院，先后任职于诺基亚和Rokid，从事语音算法相关工作。
目录信息
绪论······································································· 1
第1章 信号处理··············································· 7
1.1 数字和模拟频率··········································· 7
1.2 离散傅里叶变换···········································8
1.2.1 实数DFT ······································ 9
1.2.2 复数DFT ···································· 10
1.2.3 负频分量····································· 10
1.2.4 DFT变换性质···························· 10
1.3 FFT···························································· 11
1.3.1 FFT 结果举例····························· 12
1.3.2 实信号FFT································· 13
1.3.3 短时傅里叶变换························· 14
1.3.4 STFT语音窗函数选择··············· 14
1.4 重叠相加法和重叠保留法·························· 16
1.4.1 OLA············································· 17
1.4.2 OLS ············································· 19
1.5 加权重叠相加法········································· 21
1.5.1 WOLA 计算过程························ 22
1.5.2 WOLA 窗函数选择···················· 22
1.6 滤波器组···················································· 23
1.7 语音预加重····································· 27
1.8 高斯分布···················································· 27
1.8.1 单高斯分布································· 27
1.8.2 多维高斯分布····························· 29
1.9 HMM模型················································· 31
1.10 卡尔曼滤波·············································· 32
本章小结······························································ 33
参考文献······························································ 33
第2章 发音机理和器件································ 34
2.1 语音的产生和接收········································· 34
2.1.1 语音产生机理····························· 34
2.1.2 发声模型····································· 36
2.1.3 发音单位····································· 36
2.1.4 发音分类····································· 37
2.1.5 声音接收····································· 37
2.1.6 声音传播····································· 38
2.2 扬声器························································ 38
2.2.1 电学性能····································· 38
2.2.2 声学性能····································· 39
2.2.3 底噪············································· 40
2.2.4 频响特性····································· 41
2.2.5 THD+N POUT···························· 41
2.2.6 电压（功率）和失真················· 42
2.3 麦克风························································ 42
2.3.1 麦克风性能指标························· 42
2.3.2 麦克风的选择····························· 43
2.4 结构设计····················································45
2.4.1 扬声器相关音腔设计················· 45
2.4.2 麦克风和扬声器························· 45
2.5 音频设备···················································· 46
2.5.1 听音设备····································· 46
2.5.2 声场表现力································· 47
2.5.3 发声设备····································· 48
2.5.4 消声室测试································· 48
2.6 声学测试···················································· 49
2.6.1 声学音量····································· 50
2.6.2 失真度THD································ 50
2.6.3 频响混叠····································· 51
2.6.4 麦克风阵列一致性····················· 53
2.6.5 AEC参考通路···························· 54
2.6.6 扬声器镜频································· 56
2.6.7 扬声器最大幅度下的THD········ 57
本章小结······························································ 58
参考文献······························································ 58
第3章 语音端点检测····································· 59
3.1 特征选取···················································· 59
3.2 判决准则···················································· 61
3.2.1 门限············································· 61
3.2.2 统计模型法································· 61
3.2.3 机器学习法································· 62
3.3 VAD 实例·················································· 63
3.3.1 高斯分布····································· 63
3.3.2 算法流程····································· 63
3.3.3 计算流程····································· 68
3.4 语音/非语音帧的初始参数························· 75
3.4.1 模型参数计算····························· 75
3.4.2 高斯混合模型····························· 76
3.4.3 EM算法······································ 76
本章小结······························································ 78
参考文献······························································ 78
第4章 单通道降噪········································· 79
4.1 谱减法························································ 79
4.1.1 谱减法原理································· 79
4.1.2 谱减法实现································· 81
4.1.3 音乐噪声控制····························· 83
4.1.4 滤波法········································· 83
4.2 维纳滤波···················································· 84
4.3 子空间降噪················································ 86
4.4 WebRTC 单通道降噪实现······················· 87
4.4.1 算法原理····································· 87
4.4.2 算法初始化································· 88
4.4.3 信噪比计算：ComputeSnr ········ 90
4.4.4 语音噪声概率计算····················· 91
4.4.5 特征选取····································· 94
4.4.6 平坦度计算································· 96
4.4.7 噪声估计更新函数：
UpdateNoiseEstimate················ 97
4.4.8 消除噪声····································· 98
4.4.9 信号合成····································· 99
4.4.10 仿真结果··································· 99
4.5 深度学习降噪········································· 101
本章小结···························································· 104
参考文献···························································· 105
第5章 声学回声消除·································· 106
5.1 回声消除原理·········································· 106
5.2 自适应滤波器·········································· 108
5.2.1 维纳滤波器······························· 108
5.2.2 LMS算法································· 109
5.2.3 NLMS算法······························· 110
5.2.4 PBFDAF 算法··························· 111
5.3 WebRTC 回声消除算法························ 113
5.3.1 延迟估计··································· 113
5.3.2 自适应滤波······························· 114
5.3.3 非线性处理（NLP）··············· 117
5.3.4 MATLAB代码解读················· 118
5.3.5 仿真实验··································· 127
5.4 Speex 回声消除算法······························ 128
5.4.1 变步长计算······························· 129
5.4.2 双线性滤波器及预处理··········· 130
5.4.3 MATLAB代码解读················· 132
5.4.4 算法流程示意图······················· 141
5.4.5 仿真实验··································· 144
本章小结···························································· 146
参考文献···························································· 146
第6章 声源定位··········································· 147
6.1 GCC算法······················ 147
6.2 SRP-PHAT算法··································· 149
6.3 MUSIC算法············································ 150
6.4 TOPS 算法·············································· 152
6.5 FRIDA算法············································· 154
6.6 后处理抗噪·············································· 155
6.6.1 统计方法··································· 155
6.6.2 卡尔曼方法······························· 156
6.6.3 声源定位建模··························· 158
6.6.4 粒子滤波法······························· 160
本章小结···························································· 160
参考文献···························································· 161
第7章 波束形成技术··································· 162
7.1 麦克风阵列·············································· 163
7.1.1 麦克风数量和间距··················· 163
7.1.2 空域混叠··································· 165
7.1.3 波束形成指标··························· 165
7.1.4 噪声场······································· 166
7.1.5 声辐射······································· 167
7.2 常见波束形成方法··································· 168
7.2.1 延迟和波束形成方法··············· 168
7.2.2 滤波和波束形成方法··············· 169
7.2.3 恒定宽度波束形成方法··········· 169
7.2.4 超分辨波束形成方法··············· 170
7.2.5 广义旁瓣相消波束形成方法··· 171
7.2.6 最小方差信号无畸变响应波束形成方法················· 172
7.3 WebRTC 波束形成实例························ 174
7.3.1 编译测试文件··························· 174
7.3.2 测试文件处理流程··················· 175
7.3.3 测试命令··································· 176
7.3.4 算法的基本思想······················· 176
7.3.5 测试源码··································· 178
7.3.6 算法处理流程··························· 181
7.3.7 权重计算函数··························· 185
7.3.8 权重相乘操作··························· 186
7.4 后置滤波（Post-filtering） ·················· 187
7.4.1 MMSE后置滤波······················ 189
7.4.2 Zelinski 后置滤波····················· 190
7.4.3 mccowan后置滤波·················· 191
7.4.4 STSA后置滤波························ 192
本章小结···························································· 193
参考文献···························································· 194
第8章 盲源分离··········································· 196
8.1 基本概念及数学预备知识······················· 196
8.1.1 ICA基本概念··························· 196
8.1.2 梯度和最优化方法··················· 197
8.2 盲语音分离预处理——PCA··················· 199
8.3 频域独立成分分析法——FDICA··········· 200
8.3.1 频域ICA··································· 200
8.3.2 去相关估计方法······················· 200
8.3.3 不确定性问题··························· 201
8.4 后置滤波处理··········································· 205
8.4.1 噪声估计··································· 205
8.4.2 衰减因子计算··························· 206
8.5 GSC 与ICA联合估计···························· 209
8.5.1 峭度··········································· 209
8.5.2 经典GSC·································· 210
8.5.3 动态权重向量估计··················· 210
本章小结···························································· 212
参考文献···························································· 213
第9章 音效处理··········································· 214
9.1 声道的分类·············································· 214
9.1.1 单声道······································· 214
9.1.2 双声道······································· 215
9.1.3 立体声······································· 215
9.1.4 多声道······································· 215
9.1.5 全景声······································· 216
9.2 后端音效处理··········································· 217
本章小结···························································· 226
参考文献···························································· 226
第10章 语音编/解码··································· 227
10.1 LPC 编码·············································· 230
10.2 SILK编/解码········································· 231
10.2.1 编码参数································· 232
10.2.2 编码器····································· 234
10.2.3 解码器····································· 239
10.3 opus 编/解码概览································· 239
10.3.1 opus 解码································ 242
10.3.2 opus 编码································ 243
10.3.3 opus 语音/音乐检测·············· 244
10.4 语音质量评估········································ 247
10.4.1 主观测试································· 248
10.4.2 客观测试································· 248
10.4.3 无参考质量评估····················· 249
本章小结···························································· 249
参考文献···························································· 249
第11章 语音网络传输································ 251
11.1 拥塞控制················································ 252
11.1.1 GoogleCC拥塞控制··············· 255
11.1.2 基于PCC的拥塞控制··········· 260
11.1.3 基于BBR 的拥塞控制··········· 264
11.2 NetEQ ·················································· 266
11.2.1 NetEQ原理····························· 266
11.2.2 抖动和收包····························· 268
11.2.3 NetEQ代码框架····················· 269
11.2.4 延迟计算································· 272
11.2.5 DSP 处理································ 274
11.2.6 变速不变调····························· 275
本章小结···························································· 277
参考文献···························································· 277
第12章 语音唤醒········································ 278
12.1 语音唤醒技术简介································· 278
12.2 特征提取················································ 279
12.2.1 FBank ······································ 279
12.2.2 MFCC······································ 283
12.2.3 PCEN ······································ 284
12.3 模型结构················································ 284
12.3.1 DNN ········································ 284
12.3.2 CNN ········································ 286
12.3.3 CRNN······································ 287
12.3.4 DSCNN ··································· 288
12.3.5 子带CNN ······························· 289
12.3.6 Attention·································· 290
12.4 计算加速················································ 292
12.4.1 硬件资源评估························· 292
12.4.2 加速方向································· 294
本章小结···························································· 299
参考文献···························································· 299
第13章 语音识别········································ 301
13.1 语音特征提取········································ 303
13.1.1 MFCC特征····························· 304
13.1.2 PLP 特征································· 305
13.1.3 归一化····································· 306
13.2 声学模型················································ 306
13.2.1 高斯混合模型························· 307
13.2.2 参数估计································· 307
13.2.3 隐马尔科夫模型····················· 308
13.2.4 Baum-Welch法······················· 309
13.2.5 HMM识别器·························· 309
13.3 语言模型················································ 310
13.3.1 N-gram语言模型··················· 311
13.3.2 加权有限状态转换机············· 312
13.4 YES 和NO识别实例···························312
13.4.1 数据准备································· 312
13.4.2 数据预处理····························· 313
13.4.3 词汇和发音词典····················· 314
13.4.4 语言学模型····························· 315
13.4.5 特征提取································· 319
13.4.6 声学模型训练························· 320
13.4.7 解码和测试····························· 321
13.5 Kaldi 中文语音识别······························321
13.5.1 数据集准备····························· 321
13.5.2 声学模型训练························· 322
13.5.3 安装portaudio ························ 322
13.5.4 在线识别································· 323
13.6 DeepSpeech 语音识别······················· 324
13.6.1 识别建模································· 325
13.6.2 网络组成································· 325
13.6.3 模型训练和部署····················· 326
本章小结···························································· 330
参考文献···························································· 330
附录A 本书涉及的专业术语··························· 331
· · · · · · (收起)
第1章 信号处理··············································· 7
1.1 数字和模拟频率··········································· 7
1.2 离散傅里叶变换···········································8
1.2.1 实数DFT ······································ 9
1.2.2 复数DFT ···································· 10
1.2.3 负频分量····································· 10
1.2.4 DFT变换性质···························· 10
1.3 FFT···························································· 11
1.3.1 FFT 结果举例····························· 12
1.3.2 实信号FFT································· 13
1.3.3 短时傅里叶变换························· 14
1.3.4 STFT语音窗函数选择··············· 14
1.4 重叠相加法和重叠保留法·························· 16
1.4.1 OLA············································· 17
1.4.2 OLS ············································· 19
1.5 加权重叠相加法········································· 21
1.5.1 WOLA 计算过程························ 22
1.5.2 WOLA 窗函数选择···················· 22
1.6 滤波器组···················································· 23
1.7 语音预加重····································· 27
1.8 高斯分布···················································· 27
1.8.1 单高斯分布································· 27
1.8.2 多维高斯分布····························· 29
1.9 HMM模型················································· 31
1.10 卡尔曼滤波·············································· 32
本章小结······························································ 33
参考文献······························································ 33
第2章 发音机理和器件································ 34
2.1 语音的产生和接收········································· 34
2.1.1 语音产生机理····························· 34
2.1.2 发声模型····································· 36
2.1.3 发音单位····································· 36
2.1.4 发音分类····································· 37
2.1.5 声音接收····································· 37
2.1.6 声音传播····································· 38
2.2 扬声器························································ 38
2.2.1 电学性能····································· 38
2.2.2 声学性能····································· 39
2.2.3 底噪············································· 40
2.2.4 频响特性····································· 41
2.2.5 THD+N POUT···························· 41
2.2.6 电压（功率）和失真················· 42
2.3 麦克风························································ 42
2.3.1 麦克风性能指标························· 42
2.3.2 麦克风的选择····························· 43
2.4 结构设计····················································45
2.4.1 扬声器相关音腔设计················· 45
2.4.2 麦克风和扬声器························· 45
2.5 音频设备···················································· 46
2.5.1 听音设备····································· 46
2.5.2 声场表现力································· 47
2.5.3 发声设备····································· 48
2.5.4 消声室测试································· 48
2.6 声学测试···················································· 49
2.6.1 声学音量····································· 50
2.6.2 失真度THD································ 50
2.6.3 频响混叠····································· 51
2.6.4 麦克风阵列一致性····················· 53
2.6.5 AEC参考通路···························· 54
2.6.6 扬声器镜频································· 56
2.6.7 扬声器最大幅度下的THD········ 57
本章小结······························································ 58
参考文献······························································ 58
第3章 语音端点检测····································· 59
3.1 特征选取···················································· 59
3.2 判决准则···················································· 61
3.2.1 门限············································· 61
3.2.2 统计模型法································· 61
3.2.3 机器学习法································· 62
3.3 VAD 实例·················································· 63
3.3.1 高斯分布····································· 63
3.3.2 算法流程····································· 63
3.3.3 计算流程····································· 68
3.4 语音/非语音帧的初始参数························· 75
3.4.1 模型参数计算····························· 75
3.4.2 高斯混合模型····························· 76
3.4.3 EM算法······································ 76
本章小结······························································ 78
参考文献······························································ 78
第4章 单通道降噪········································· 79
4.1 谱减法························································ 79
4.1.1 谱减法原理································· 79
4.1.2 谱减法实现································· 81
4.1.3 音乐噪声控制····························· 83
4.1.4 滤波法········································· 83
4.2 维纳滤波···················································· 84
4.3 子空间降噪················································ 86
4.4 WebRTC 单通道降噪实现······················· 87
4.4.1 算法原理····································· 87
4.4.2 算法初始化································· 88
4.4.3 信噪比计算：ComputeSnr ········ 90
4.4.4 语音噪声概率计算····················· 91
4.4.5 特征选取····································· 94
4.4.6 平坦度计算································· 96
4.4.7 噪声估计更新函数：
UpdateNoiseEstimate················ 97
4.4.8 消除噪声····································· 98
4.4.9 信号合成····································· 99
4.4.10 仿真结果··································· 99
4.5 深度学习降噪········································· 101
本章小结···························································· 104
参考文献···························································· 105
第5章 声学回声消除·································· 106
5.1 回声消除原理·········································· 106
5.2 自适应滤波器·········································· 108
5.2.1 维纳滤波器······························· 108
5.2.2 LMS算法································· 109
5.2.3 NLMS算法······························· 110
5.2.4 PBFDAF 算法··························· 111
5.3 WebRTC 回声消除算法························ 113
5.3.1 延迟估计··································· 113
5.3.2 自适应滤波······························· 114
5.3.3 非线性处理（NLP）··············· 117
5.3.4 MATLAB代码解读················· 118
5.3.5 仿真实验··································· 127
5.4 Speex 回声消除算法······························ 128
5.4.1 变步长计算······························· 129
5.4.2 双线性滤波器及预处理··········· 130
5.4.3 MATLAB代码解读················· 132
5.4.4 算法流程示意图······················· 141
5.4.5 仿真实验··································· 144
本章小结···························································· 146
参考文献···························································· 146
第6章 声源定位··········································· 147
6.1 GCC算法······················ 147
6.2 SRP-PHAT算法··································· 149
6.3 MUSIC算法············································ 150
6.4 TOPS 算法·············································· 152
6.5 FRIDA算法············································· 154
6.6 后处理抗噪·············································· 155
6.6.1 统计方法··································· 155
6.6.2 卡尔曼方法······························· 156
6.6.3 声源定位建模··························· 158
6.6.4 粒子滤波法······························· 160
本章小结···························································· 160
参考文献···························································· 161
第7章 波束形成技术··································· 162
7.1 麦克风阵列·············································· 163
7.1.1 麦克风数量和间距··················· 163
7.1.2 空域混叠··································· 165
7.1.3 波束形成指标··························· 165
7.1.4 噪声场······································· 166
7.1.5 声辐射······································· 167
7.2 常见波束形成方法··································· 168
7.2.1 延迟和波束形成方法··············· 168
7.2.2 滤波和波束形成方法··············· 169
7.2.3 恒定宽度波束形成方法··········· 169
7.2.4 超分辨波束形成方法··············· 170
7.2.5 广义旁瓣相消波束形成方法··· 171
7.2.6 最小方差信号无畸变响应波束形成方法················· 172
7.3 WebRTC 波束形成实例························ 174
7.3.1 编译测试文件··························· 174
7.3.2 测试文件处理流程··················· 175
7.3.3 测试命令··································· 176
7.3.4 算法的基本思想······················· 176
7.3.5 测试源码··································· 178
7.3.6 算法处理流程··························· 181
7.3.7 权重计算函数··························· 185
7.3.8 权重相乘操作··························· 186
7.4 后置滤波（Post-filtering） ·················· 187
7.4.1 MMSE后置滤波······················ 189
7.4.2 Zelinski 后置滤波····················· 190
7.4.3 mccowan后置滤波·················· 191
7.4.4 STSA后置滤波························ 192
本章小结···························································· 193
参考文献···························································· 194
第8章 盲源分离··········································· 196
8.1 基本概念及数学预备知识······················· 196
8.1.1 ICA基本概念··························· 196
8.1.2 梯度和最优化方法··················· 197
8.2 盲语音分离预处理——PCA··················· 199
8.3 频域独立成分分析法——FDICA··········· 200
8.3.1 频域ICA··································· 200
8.3.2 去相关估计方法······················· 200
8.3.3 不确定性问题··························· 201
8.4 后置滤波处理··········································· 205
8.4.1 噪声估计··································· 205
8.4.2 衰减因子计算··························· 206
8.5 GSC 与ICA联合估计···························· 209
8.5.1 峭度··········································· 209
8.5.2 经典GSC·································· 210
8.5.3 动态权重向量估计··················· 210
本章小结···························································· 212
参考文献···························································· 213
第9章 音效处理··········································· 214
9.1 声道的分类·············································· 214
9.1.1 单声道······································· 214
9.1.2 双声道······································· 215
9.1.3 立体声······································· 215
9.1.4 多声道······································· 215
9.1.5 全景声······································· 216
9.2 后端音效处理··········································· 217
本章小结···························································· 226
参考文献···························································· 226
第10章 语音编/解码··································· 227
10.1 LPC 编码·············································· 230
10.2 SILK编/解码········································· 231
10.2.1 编码参数································· 232
10.2.2 编码器····································· 234
10.2.3 解码器····································· 239
10.3 opus 编/解码概览································· 239
10.3.1 opus 解码································ 242
10.3.2 opus 编码································ 243
10.3.3 opus 语音/音乐检测·············· 244
10.4 语音质量评估········································ 247
10.4.1 主观测试································· 248
10.4.2 客观测试································· 248
10.4.3 无参考质量评估····················· 249
本章小结···························································· 249
参考文献···························································· 249
第11章 语音网络传输································ 251
11.1 拥塞控制················································ 252
11.1.1 GoogleCC拥塞控制··············· 255
11.1.2 基于PCC的拥塞控制··········· 260
11.1.3 基于BBR 的拥塞控制··········· 264
11.2 NetEQ ·················································· 266
11.2.1 NetEQ原理····························· 266
11.2.2 抖动和收包····························· 268
11.2.3 NetEQ代码框架····················· 269
11.2.4 延迟计算································· 272
11.2.5 DSP 处理································ 274
11.2.6 变速不变调····························· 275
本章小结···························································· 277
参考文献···························································· 277
第12章 语音唤醒········································ 278
12.1 语音唤醒技术简介································· 278
12.2 特征提取················································ 279
12.2.1 FBank ······································ 279
12.2.2 MFCC······································ 283
12.2.3 PCEN ······································ 284
12.3 模型结构················································ 284
12.3.1 DNN ········································ 284
12.3.2 CNN ········································ 286
12.3.3 CRNN······································ 287
12.3.4 DSCNN ··································· 288
12.3.5 子带CNN ······························· 289
12.3.6 Attention·································· 290
12.4 计算加速················································ 292
12.4.1 硬件资源评估························· 292
12.4.2 加速方向································· 294
本章小结···························································· 299
参考文献···························································· 299
第13章 语音识别········································ 301
13.1 语音特征提取········································ 303
13.1.1 MFCC特征····························· 304
13.1.2 PLP 特征································· 305
13.1.3 归一化····································· 306
13.2 声学模型················································ 306
13.2.1 高斯混合模型························· 307
13.2.2 参数估计································· 307
13.2.3 隐马尔科夫模型····················· 308
13.2.4 Baum-Welch法······················· 309
13.2.5 HMM识别器·························· 309
13.3 语言模型················································ 310
13.3.1 N-gram语言模型··················· 311
13.3.2 加权有限状态转换机············· 312
13.4 YES 和NO识别实例···························312
13.4.1 数据准备································· 312
13.4.2 数据预处理····························· 313
13.4.3 词汇和发音词典····················· 314
13.4.4 语言学模型····························· 315
13.4.5 特征提取································· 319
13.4.6 声学模型训练························· 320
13.4.7 解码和测试····························· 321
13.5 Kaldi 中文语音识别······························321
13.5.1 数据集准备····························· 321
13.5.2 声学模型训练························· 322
13.5.3 安装portaudio ························ 322
13.5.4 在线识别································· 323
13.6 DeepSpeech 语音识别······················· 324
13.6.1 识别建模································· 325
13.6.2 网络组成································· 325
13.6.3 模型训练和部署····················· 326
本章小结···························································· 330
参考文献···························································· 330
附录A 本书涉及的专业术语··························· 331
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读后感
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概念描述浅尝辄止，无法掌握要点。每一个章节都很难看下去，逻辑性差，感觉就是一些材料的堆叠，前因后果没讲清楚，看得云里雾里。作者既想讲好背后原理，又想具备实操性，结果两样都没讲明白，作者的水平是个问号。我觉得并不需要面面俱到，把几个关键的技术讲透彻，比如回声...
评分概念描述浅尝辄止，无法掌握要点。每一个章节都很难看下去，逻辑性差，感觉就是一些材料的堆叠，前因后果没讲清楚，看得云里雾里。作者既想讲好背后原理，又想具备实操性，结果两样都没讲明白，作者的水平是个问号。我觉得并不需要面面俱到，把几个关键的技术讲透彻，比如回声...
评分概念描述浅尝辄止，无法掌握要点。每一个章节都很难看下去，逻辑性差，感觉就是一些材料的堆叠，前因后果没讲清楚，看得云里雾里。作者既想讲好背后原理，又想具备实操性，结果两样都没讲明白，作者的水平是个问号。我觉得并不需要面面俱到，把几个关键的技术讲透彻，比如回声...
评分概念描述浅尝辄止，无法掌握要点。每一个章节都很难看下去，逻辑性差，感觉就是一些材料的堆叠，前因后果没讲清楚，看得云里雾里。作者既想讲好背后原理，又想具备实操性，结果两样都没讲明白，作者的水平是个问号。我觉得并不需要面面俱到，把几个关键的技术讲透彻，比如回声...
评分概念描述浅尝辄止，无法掌握要点。每一个章节都很难看下去，逻辑性差，感觉就是一些材料的堆叠，前因后果没讲清楚，看得云里雾里。作者既想讲好背后原理，又想具备实操性，结果两样都没讲明白，作者的水平是个问号。我觉得并不需要面面俱到，把几个关键的技术讲透彻，比如回声...
用户评价
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这本书的作者背景和写作风格，是我在选择技术书籍时非常看重的一点。虽然我还没有深入阅读，但从书名和初步的印象来看，它似乎是出自一位在语音处理领域有着丰富实践经验的专家之手。我期待书中能够展现出一种严谨又不失风趣的写作风格。理论性的讲解要做到准确无误，但同时也要避免过于枯燥乏味，能够用生动形象的比喻或者类比来帮助读者理解。在“实践指南”这个定位下，我特别希望作者能够分享一些他在实际项目中遇到的挑战和解决方案，这比单纯的书本理论更有价值。比如，在处理大规模语音数据集时，有哪些高效的预处理方法？在部署实时语音处理系统时，又有哪些需要注意的性能优化问题？如果书中能提供一些“避坑指南”，那将是极大的福音。我猜想，这本书的读者群体可能包括在校学生、研究人员，以及希望将语音技术应用到实际产品中的开发者。因此，作者在内容的选择和深度的把握上，应该会尽量兼顾不同读者的需求。
评分我对这本书的预期，主要集中在其“指南”的实用性和“实践”的可操作性上。我希望它不是一本只能放在书架上“摆设”的书，而是能够成为我进行语音处理项目时，能够随手翻阅的“工具书”。我期待它能够提供一套清晰的学习路径，让我能够从零开始，逐步掌握实时语音处理的各项技术。例如，在语音信号的采集和预处理方面，是否能提供详细的步骤和代码示例？在语音识别的声学模型和语言模型构建上，是否能介绍目前主流的方法和相应的工具库？在实时性方面，如何保证低延迟的语音处理？这些都是我非常关心的问题。我还希望书中能够涵盖一些进阶内容，比如如何利用深度学习模型来提升语音识别的准确率，或者如何开发更自然的语音合成系统。总之，我希望这本书能够给我带来切实的帮助，让我能够学有所成，并且能够将所学知识应用到实际的开发工作中，解决我遇到的实际问题。一本优秀的实践指南，应该能够让读者在看完之后，立刻有信心去动手尝试，去创造一些东西。
评分这本书的章节安排给我的第一印象是逻辑清晰，循序渐进。从目录上看，它似乎从最基础的概念讲起，然后逐渐深入到更复杂的算法和应用场景。这对于初学者来说非常友好，可以避免一开始就面对大量晦涩难懂的专业术语而感到沮丧。我猜想，书中应该会涉及一些关于声学原理、信号处理基础的内容，这是理解语音处理的基石。然后，可能会讲到语音识别（ASR）和语音合成（TTS）等核心技术，并且会对不同的模型和算法进行比较分析。特别吸引我的是“实践指南”这四个字，这意味着它应该会提供一些可操作的步骤和工具介绍。我非常期待书中能包含一些实际项目案例，比如如何搭建一个简单的语音助手，或者如何实现一个实时字幕生成器。如果书中能提供相关的代码库或者GitHub链接，那就更棒了！这样我就可以直接下载代码，然后跟着书中的讲解一步步地进行调试和修改，从而加深理解。我觉得，一本好的技术实践指南，不应该仅仅停留在理论层面，更重要的是要能够引领读者走上实践的道路，解决实际问题。这本书的标题和目录结构，给我留下了这样的好印象，让我觉得它很有可能达到这个目标。
评分这本书的封面设计倒是挺吸引人的，整体色调比较沉稳，搭配着一些科技感的线条，一眼就能看出是跟技术相关的书籍。标题“实时语音处理实践指南”本身就点明了主题，对于我这种对语音技术一直很好奇但又不知从何下手的人来说，这种直接明了的标题会让我有想要翻开看看的冲动。包装也很严实，快递小哥送来的时候，我特意检查了一下，没有任何磕碰的痕迹，这点很满意。拿到手里，纸张的质感也还不错，不是那种特别粗糙的纸，印刷清晰，字体大小也很适中，读起来不会费眼睛。我尤其喜欢它标题中“实践”这两个字，意味着这本书不会是那种纯理论、枯燥乏味的读物，而是更侧重于实际操作和应用。我希望里面能有很多的代码示例，最好能配上详细的解释，让我能边学边练，快速上手。现在市面上的技术书籍很多，但真正能做到理论与实践结合，并且讲解得深入浅出的，并不多见。所以，对于这本书，我最期待的就是它能否真正帮助我理解实时语音处理的核心技术，并且能够通过实际的例子，让我掌握构建相关应用的基本方法。毕竟，光说不练假把式，只有亲手实现，才能真正领会到其中的奥妙。
评分这本书的排版和设计风格，给我的感觉是专业且现代。它没有采用那种过于花哨的图表，而是以清晰、简洁的图示来辅助说明复杂的概念，这一点我很欣赏。我一直认为，好的技术书籍，应该在视觉呈现上做到既能吸引人，又不至于喧宾夺物，而是恰如其分地服务于内容本身。书中大量的公式和算法推导，如果能够配以恰当的流程图或者示意图，会更容易理解。我尤其关注书中是否对一些关键的算法，比如MFCC、HMM、DNN在语音处理中的应用，进行了详细的解释和示例。毕竟，这些是语音识别中最核心的部分。我希望书中不仅会介绍这些算法的原理，还会提供一些如何在实际项目中应用这些算法的建议。另外，“实践指南”这几个字，让我对书中的“实战”部分寄予厚望。我期待能够看到一些真实的、可运行的代码片段，最好是能够覆盖到不同的编程语言和常用的开发框架，例如Python配合TensorFlow或PyTorch。如果书中能讲解如何对模型进行调优，如何处理噪音数据，以及如何评估模型性能等实用技巧，那这本书的价值就更大了。
评分学得太宽泛笼统，很一般
评分学得太宽泛笼统，很一般
评分概念描述晦涩难懂，逻辑性太差。什么都是浅尝辄止，算不上一本好书。
评分学得太宽泛笼统，很一般
评分学得太宽泛笼统，很一般
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