Dissertation for the Doctoral Degree in Engineering
Voice signal de-noise base on wavelet and arithmetic implement with DSP
Candidate:
Yan Feng
Supervisor:
We study the application of wavelet in voice signal de-noising, focus on the wavelet shrinkage de-noising. There are three main wavelet de-noising methods at present, they are wavelet shrinkage de-noising, model-max de-noising and spatial selection de-noising. wavelet shrinkage de-noising method has a small amount calculation and good de-noising effect, so it has got wide application. But the selection of de-noising threshold directly related to the de-noising effect. Some wavelet coefficients can't be set zero when the threshold is undersize and parts of noises are retained; some useful signals will be reduced if the threshold is up-size. These cases may degrade the de-noising effect.
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哈尔滨工业大学工学硕士学位论文
We design real time de-noising method, and transplant the above-mentioned algorithms to DSP with SEED-EDC6713 module. We completed spectral subtraction, wavelet shrinkage de-noising and the method this article put forward, verify the simulation results. Keywords wavelet shrinkage de-noising; voice signal de-noising;
我们将仿真的算法移植到 DSP 平台中，利用 SEED-DEC6713 模块，设 计实时算法，实现了谱减法、小波阈值法以及我们所提出的方法，验证了仿 真效果。 关键词 小波阈值去噪；语音信号去噪；谱减法；DSP
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哈尔滨工业பைடு நூலகம்学工学硕士学位论文
Abstract
Wavelet analysis is a mathematical analysis tools which was developed in the 1990s, because of its excellent time-frequency analysis ability.Wavelet analysis has undergone a unprecedented development in the signal processing field. As an important branch of wavelet analysis, wavelet de-noising theory has also got a great development and application. Voice signal de-noising is an important area of voice signal processing, generally as a pretreatment module exists in the system. Scholars often carry through study on the basis of broad band plus noise, and has brought forward many voice de-noising methods. Although in theory, still not completely solve the problem of voice de-noising, some methods have been proved to be effective in practice. Wavelet analysis can simultaneously analyse signal in time and frequency domain, so it can effectively achieve the voice signal de-noising.
第 1 章 绪论................................................................................................................ 1 1.1 课题背景........................................................................................................... 1 1.1.1 语音去噪的意义........................................................................................ 1 1.1.2 语音去噪的发展现状................................................................................ 2 1.2 小波分析的发展和应用................................................................................... 4 1.2.1 小波分析的发展........................................................................................ 4 1.2.2 小波分析的应用........................................................................................ 5 1.3 小波分析在语音信号去噪方面的应用........................................................... 6 1.4 课题研究内容................................................................................................... 7
本文重点研究了小波阈值法，针对不同的阈值函数的选取、阈值处理方 法及小波函数的选取做了研究。针对阈值法中高频信号失真的缺点，我们对 小尺度上的小波系数做谱减法预处理，之后以一个小阈值去除剩余噪声，大 尺度上仍然利用阈值法处理。经过仿真实验表明，这种处理方法较传统的小 波阈值法，保留了更多有用信号，减小了去噪后语音信号的失真。
本文研究小波在语音信号去噪方面的应用，重点研究小波阈值语音去 噪。目前已经提出的小波去噪方法主要有三种，模极大值去噪、空域相关滤 波去噪以及小波阈值去噪法。阈值法具有计算量小、去噪效果好的特点，取 得了广泛的应用。然而在阈值法中，阈值的选取直接关系到去噪效果的优 劣。如果阈值选取过小，那么一部分噪声小波系数将不能被置零，从而在去 噪后的信号中保留了部分噪声信息；如果阈值选的偏大，则会将一部分有用 信号去掉，使得去噪后的信号丢失信息。
Prof. Wang Qi
Academic Degree Applied for: Master of Engineering
Speciality:
Instrument Science and Technology
Affiliation：
Dept. of Automatic Testing and Control
spectral subtraction; DSP
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哈尔滨工业大学工学硕士学位论文
目录
摘要 ............................................................................................................................... I Abstract ....................................................................................................................... V
导
师：
申 请 学 位：
学 科、专 业：
所 在 单 位：
答 辩 日 期：
授予学位单位：
闫峰 王祁 教授 工学硕士 测试计量技术及仪器 自动化测试与控制系 2008 年 6 月 哈尔滨工业大学
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哈尔滨工业大学工学硕士学位论文
Classified Index: TB535 U.D.C: 621.3
This article focuses on the wavelet shrinkage de-noising, study different threshold function, different threshold approach and the selection of mother wavelet. For the shortcoming of high frequency signal distortion in the wavelet shrinkage de-noising, we use spectral subtraction to treat with small scales wavelet coefficients and then remove the residual noise with a small threshold; in big scales, we still use threshold method directly. The simulation shows that this method has a better effect than wavelet shrinkage de-noising, it could reserves more high frequency signal, reduce distortion.