一种语音增强算法的研究及实现(硕士论文)200630
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realtimerealization吉林大学硕士学位论文i目录第一章绪论111课题研究的背景112语音增强算法综述2121基于多通道输入的语音增强算法3122基于单通道输入的语音增强算法413全文结构安排12第二章语音增强的基本理论1421语音的特性1422噪声的分类及特性1623语音增强效果的评测方法1924语音增强系统19第三章信号子空间的语音增强算法2131信号子空间原理21311信号和噪声模型21312信号和噪声子空间2232白噪声干扰下信号的线性估计2433有色噪声干扰下信号的线性估计28小结29第四章基于预处理vad技术和自适应kl变换的语音增强算法3041有色噪声干扰下的信号估计3042有色噪声的近似模型3243算法的实现33431klt自适应跟踪算法34432噪声和纯净信号能量的估计37433vad的实现38吉林大学硕士学位论文ii小结41第五章仿真实验4351白噪声和有色噪声干扰的增强效果分析4352自适应klt算法和改进减谱法的比较47521语音谱畸变和噪声整形畸变的分析47522信噪比的比较50第六章增强算法的dsp实现及结论5261硬件系统52611dsk简介52612音频接口芯片tlc320ad50c53613多通道缓冲串行口mcbsp5462应用程序设计56621系统初始化程序设计57622高级c语言程序设计57623汇编语言程序设计59624c语言和汇编语言混合编程5963性能测试6064结论和展望60致谢63参考文献64中文摘要i英文摘要iv吉林大学硕士学位论文1第一章绪论11课题研究的背景当今世界正处于信息时代
吉林大学硕士学位论文
目
录
第一章 绪论 ···································································································1 1.1 课题研究的背景 ···················································································1 1.2 语音增强算法综述 ················································································2 1.2.1 基于多通道输入的语音增强算法 ················································3 1.2.2 基于单通道输入的语音增强算法 ················································4 1.3 全文结构安排 ·····················································································12 第二章 语音增强的基本理论 ······································································14 2.1 语音的特性·························································································14 2.2 噪声的分类及特性 ·············································································16 2.3 语音增强效果的评测方法 ·································································19 2.4 语音增强系统 ·····················································································19 第三章 信号子空间的语音增强算法 ··························································21 3.1 信号子空间原理 ·················································································21 3.1.1 信号和噪声模型 ··········································································21 3.1.2 信号和噪声子空间 ······································································22 3.2 白噪声干扰下信号的线性估计 ·························································24 3.3 有色噪声干扰下信号的线性估计 ·····················································28 小结 ···········································································································29 第四章 基于预处理 VAD 技术和自适应 KL 变换的语音增强算法 ·········30 4.1 有色噪声干扰下的信号估计 ······························································30 4.2 有色噪声的近似模型 ·········································································32 4.3 算法的实现·························································································33 4.3.1 KLT 自适应跟踪算法 ···································································34 4.3.2 噪声和纯净信号能量的估计 ·······················································37 4.3.3 VAD 的实现 ··················································································38
Study and Implementation of Speech Enhancement Algorithm
作者姓名： 专
李宏伟
业：通信与信息系统
导师姓名 及 职 称 ： 赵晓晖 教授 丛玉良 副 教授
论文起止年月： 2001 年 12 月至 2003 年 2 月
吉林大学硕士学位论文
提
要
语音增强的目的是从带噪语音信号中压缩背景噪声， 提取纯净语音， 改进 通话质量。但是，由于人们对噪声的认识仍存在很大的局限性，很难找到一种 通用的噪声模型和统一的语音增强处理方法。 并且， 语音信号和与之特性相似 的噪声信号在数学上不易区分。所以，语音增强是一类特殊的信号估计问题。 这一问题的解决不仅与语音信号数字处理技术有关， 还涉及到对语言学和人的 听觉感知特性的深入了解。 按输入通道的不同， 语音增强算法可分为两大类： 一类是基于多通道输入 的语音增强算法； 另一类是基于单通道输入的语音增强算法。 本文提出了一种 基于预处理 VAD 技术和自适应 KL 变换的语音增强算法。该算法是一种单通 道输入、 针对加性有色噪声干扰的增强算法。 算法中首先运用自适应 KL 变换， 将有色加性带噪语音沿纯净语音的向量空间进行分解。 根据特征向量上语音和 噪声信号的能量来调整每个 KL 变换后的分量。采用预处理技术的语音活动性 检测 VAD(Voice Activity Detection)算法来检测噪声帧，用于完成后续语音 帧中噪声能量的估计。变换后的分量调整遵循频域约束最优化准则。最后用 KL 逆变换估计出增强后的语音信号。 算法首先运用 MATLAB 进行仿真，验证了理论上的有效性。然后，在 TI 公司的 DSK 板上进行了实时实现。 客观测试和主观试听表明， 算法对于有色噪 声干扰下的带噪语音信号有较好的增强效果。 关键词：语音增强 自适应 KL 变换 语音活动性检测 DSK 实时实现
吉林大学硕士学位论文
论文分类号 TN912.3 密 级 内 部 2200630
单 位 代 码 10183 研究生学号
硕 士 学 位 论 文 吉 林 大 学 作 者 李宏伟
吉
硕 士
林
学
大
位
学
论 文
ቤተ መጻሕፍቲ ባይዱ
一种语音增强算法的研究及实现
吉林大学硕士学位论文
Abstract
The aim of speech enhancement is to compress background noise, to extract pure speech, and to improve communication quality in noisy environment. Because people has limited knowledge about noise signal, it is difficult to find a general noise model and a general speech enhancement approach. Moreover, it is hard to distinguish between speech signal and some noise signal whose property is similar to speech in mathematics. So speech enhancement is a special problem in signal estimation. This problem can be solved by studying more speech signal processing technology and learning linguistics and perception property deeply. According to the difference numbers of input channel, speech enhancement algorithm can be divided into two types. One type is speech enhancement algorithm with multi-channel input. Another is speech enhancement algorithm with single input channel. A new speech enhancement algorithm is proposed, which is named adaptive KLT speech enhancement algorithm with preprocessing VAD. That algorithm is a kind of algorithm with signal input channel processing colored noise. In proposed algorithm, an adaptive KLT (Karhunen-Loeve transform) speech enhancement algorithm with preprocessing VAD is studied. In this algorithm speech signal degraded by additive colored noise is decomposed into the components by adaptive KLT along clean speech vector space. Each component is modified due to its noise and clean speech energies along each eigenvector. Noise speech frame is detected by VAD with speech preprocessing algorithm, and noise energy of next noisy speech frame is estimated. Each component is modified according to an optimization criterion of frequency domain constraint. Then inverse KLT is conducted and an estimation of the enhanced signal is synthesized. The algorithm is emulated in MATLAB, and is validated in theory. Then it is performed real time realization on DSK board of TI Incorporated. Objective test and subjective listening show that the algorithm demonstrates better performance in environment of colored noise. Key words: Speech enhancement; Adaptive Karhunen-Loeve transform; VAD; DSK; Real time realization
吉林大学硕士学位论文
目
录
第一章 绪论 ···································································································1 1.1 课题研究的背景 ···················································································1 1.2 语音增强算法综述 ················································································2 1.2.1 基于多通道输入的语音增强算法 ················································3 1.2.2 基于单通道输入的语音增强算法 ················································4 1.3 全文结构安排 ·····················································································12 第二章 语音增强的基本理论 ······································································14 2.1 语音的特性·························································································14 2.2 噪声的分类及特性 ·············································································16 2.3 语音增强效果的评测方法 ·································································19 2.4 语音增强系统 ·····················································································19 第三章 信号子空间的语音增强算法 ··························································21 3.1 信号子空间原理 ·················································································21 3.1.1 信号和噪声模型 ··········································································21 3.1.2 信号和噪声子空间 ······································································22 3.2 白噪声干扰下信号的线性估计 ·························································24 3.3 有色噪声干扰下信号的线性估计 ·····················································28 小结 ···········································································································29 第四章 基于预处理 VAD 技术和自适应 KL 变换的语音增强算法 ·········30 4.1 有色噪声干扰下的信号估计 ······························································30 4.2 有色噪声的近似模型 ·········································································32 4.3 算法的实现·························································································33 4.3.1 KLT 自适应跟踪算法 ···································································34 4.3.2 噪声和纯净信号能量的估计 ·······················································37 4.3.3 VAD 的实现 ··················································································38
Study and Implementation of Speech Enhancement Algorithm
作者姓名： 专
李宏伟
业：通信与信息系统
导师姓名 及 职 称 ： 赵晓晖 教授 丛玉良 副 教授
论文起止年月： 2001 年 12 月至 2003 年 2 月
吉林大学硕士学位论文
提
要
语音增强的目的是从带噪语音信号中压缩背景噪声， 提取纯净语音， 改进 通话质量。但是，由于人们对噪声的认识仍存在很大的局限性，很难找到一种 通用的噪声模型和统一的语音增强处理方法。 并且， 语音信号和与之特性相似 的噪声信号在数学上不易区分。所以，语音增强是一类特殊的信号估计问题。 这一问题的解决不仅与语音信号数字处理技术有关， 还涉及到对语言学和人的 听觉感知特性的深入了解。 按输入通道的不同， 语音增强算法可分为两大类： 一类是基于多通道输入 的语音增强算法； 另一类是基于单通道输入的语音增强算法。 本文提出了一种 基于预处理 VAD 技术和自适应 KL 变换的语音增强算法。该算法是一种单通 道输入、 针对加性有色噪声干扰的增强算法。 算法中首先运用自适应 KL 变换， 将有色加性带噪语音沿纯净语音的向量空间进行分解。 根据特征向量上语音和 噪声信号的能量来调整每个 KL 变换后的分量。采用预处理技术的语音活动性 检测 VAD(Voice Activity Detection)算法来检测噪声帧，用于完成后续语音 帧中噪声能量的估计。变换后的分量调整遵循频域约束最优化准则。最后用 KL 逆变换估计出增强后的语音信号。 算法首先运用 MATLAB 进行仿真，验证了理论上的有效性。然后，在 TI 公司的 DSK 板上进行了实时实现。 客观测试和主观试听表明， 算法对于有色噪 声干扰下的带噪语音信号有较好的增强效果。 关键词：语音增强 自适应 KL 变换 语音活动性检测 DSK 实时实现
吉林大学硕士学位论文
论文分类号 TN912.3 密 级 内 部 2200630
单 位 代 码 10183 研究生学号
硕 士 学 位 论 文 吉 林 大 学 作 者 李宏伟
吉
硕 士
林
学
大
位
学
论 文
ቤተ መጻሕፍቲ ባይዱ
一种语音增强算法的研究及实现
吉林大学硕士学位论文
Abstract
The aim of speech enhancement is to compress background noise, to extract pure speech, and to improve communication quality in noisy environment. Because people has limited knowledge about noise signal, it is difficult to find a general noise model and a general speech enhancement approach. Moreover, it is hard to distinguish between speech signal and some noise signal whose property is similar to speech in mathematics. So speech enhancement is a special problem in signal estimation. This problem can be solved by studying more speech signal processing technology and learning linguistics and perception property deeply. According to the difference numbers of input channel, speech enhancement algorithm can be divided into two types. One type is speech enhancement algorithm with multi-channel input. Another is speech enhancement algorithm with single input channel. A new speech enhancement algorithm is proposed, which is named adaptive KLT speech enhancement algorithm with preprocessing VAD. That algorithm is a kind of algorithm with signal input channel processing colored noise. In proposed algorithm, an adaptive KLT (Karhunen-Loeve transform) speech enhancement algorithm with preprocessing VAD is studied. In this algorithm speech signal degraded by additive colored noise is decomposed into the components by adaptive KLT along clean speech vector space. Each component is modified due to its noise and clean speech energies along each eigenvector. Noise speech frame is detected by VAD with speech preprocessing algorithm, and noise energy of next noisy speech frame is estimated. Each component is modified according to an optimization criterion of frequency domain constraint. Then inverse KLT is conducted and an estimation of the enhanced signal is synthesized. The algorithm is emulated in MATLAB, and is validated in theory. Then it is performed real time realization on DSK board of TI Incorporated. Objective test and subjective listening show that the algorithm demonstrates better performance in environment of colored noise. Key words: Speech enhancement; Adaptive Karhunen-Loeve transform; VAD; DSK; Real time realization