基于小波包变换和小波阈值消噪的语音特征提取

为了实现强噪声背景下语音信号的特征提取,根据小波变换的多分辨率特性,以及与人耳耳蜗滤渡相一致的特性,利用小波包变换,在各语音特征频率段上,提取出包含丰富的非平稳信息的语音特征;并在小波包分解去噪的基础上,构造了模糊阈值函数,利用小波模糊阈值去噪,得到了信噪比较高的语音信号.研究结果表明,小波包变换和小波阈值去噪,较好地消除了强噪声背景下的噪声,并有效地提取出了语音信号特征.     

结点阈值小波包变换语音增强新算法

人耳频率分辨率是非线性的,而小波包算法有灵活的时频分析能力,可较好的模拟人耳基底膜的频率分析特性。本文提出了一种新的基于结点阈值的小波包变换语音增强算法。采用Bark尺度小波包对含噪语音进行分解,在语音信号的子带层次上进行阈值操作,并采用软阈值方法进行阈值处理。采用谱熵法估计结点噪声。实验表明,该算法在多种噪声,尤其是有色噪声和非平稳噪声条件下均有较好的语音增强效果。     

基于小波变换的多阈值法语音去噪研究

研究了基于小波变换的语音去噪问题,在对传统阚值法去噪和语音特性分析基础上,提出一种改进的多阈值法语音信号去噪方法.仿真实验结果表明.本方法能有效去除信号中的噪声和较好保留语音细节,与谱减法去噪相比,能达到更佳的语音信号去噪效果.     

基于子波变换的自适应滤波语音增强方法

在对噪声语音信号进行多分辨率子波分析的基础上,对分解后子波变换系数在尺度上和尺度间进行自适应滤波处理,并用它们的线性组合重构其语音信号,获得一种语音增强方法.     

基于预处理VAD技术和KL变换的语音增强算法研究

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.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.实验表明,该算法对于单通道输入有色噪声干扰下的带噪语音信号有较好的增强效果.     

基于小波阈值去噪方法的研究

系统地研究了小波的软阈值去噪、硬阈值去噪、garrote阈值去噪以及semisoft阈值去噪等四种阈值去噪方法,并把它们应用到典型的含噪信号中进行比较,最后对去噪的影响进行了阐述.     

基于双正交小波变换QRS波检测方法研究与改进

心电图中QRS波是特征最明显的波形,是医学上诊断症状的重要依据,因此QRS波的正确识别成为心电检测的首要问题。由于心电信号是mV级的微弱信号,易受各种干扰影响;同时,目前常用的小波变换识别QRS波方法,计算量大,对系统要求高。针对这些问题,首先,提出使用双正交B样条小波基对心电信号进行处理,并通过在各尺度波形上检测极大值取代模极大值对的方式,搜索R波;然后,使用MIT-BIH心律失常数据库验证算法效果,并针对验证结果对原始算法进行检测策略的优化,改用自适应阈值的检测方式;同时,引进对部分可疑点搜索模极大值对的检查方法,减少假阳性的情况。算法经MIT-BIH验证,得到98.9%的准确率。     

基于小波包改进阈值方法的电能质量信号消噪

为了改善暂态电能质量扰动信号的消噪效果,提出了一种小波包改进阈值的消噪方法。在分析了软阈值消噪方法和硬阈值消噪方法的基础上,对阈值量化函数进行了改进,并通过小波包变换对消噪效果进行了强化。改进后的新阈值消噪法能有效克服＂硬阈值法不连续、软阈值法有偏差＂的缺点。对暂态电能质量扰动信号消噪处理的仿真结果表明,该新方法在消噪的同时能够减少信息的损失,在不同信噪比下都能有效地去除白噪声,其消噪效果优于软阈值函数消噪法。     

基于自适应阈值的图像去噪方法

图像去噪是图像处理中重要的一部分,小波域图像去噪是图像处理中一个引人关注的研究方向.介绍了小波变换并提出了一种基于自适应阈值图像去噪方法,并应用MATLAB软件实现了小波图像去噪的计算机仿真.     

射线图象的自适应增强算法研究

在现代无损检测技术中,图象处理技术用来定量或定性分析和发现设备的故障,并得到了普遍应用。本文根据射线图角的特点,介绍一种在实时成象检测中,对所得的图角进行空间域自适应对经度增强的算法,试验表明,该算法实时性好,能使原图象中较模糊、对比度差的细节得到明显的增强。同时根据以变换的多分辨率特性,在小波域也进行了尝试。     

基于小波包分解的阈值消噪在车载CAN总线上的应用

为了实现在车载CAN总线控制器故障产生不可预知干扰信号的情况下正确读出CAN总线上的故障码,提出了一种基于小波包分解的阈值消噪方法。将信号进行基于最小Shannon熵原理的最佳树小波包分解后,针对CAN总线传输信号特点,对高频和低频系数采取不同阈值处理的混合阈值消噪处理。对于高频系数,采用基于Stein无偏似然估计原理的自适应阈值;在干扰信号的统计特性未知的情况下,对低频系数阈值的选取采取保持频率单一和能量损耗小的双重标准。最后,由阈值处理后的系数重构信号,得到正确的传输码。实验结果表明,该混合阈值消噪处理方法对消除这种由CAN总线上的控制器故障引起的干扰,较传统的消噪技术提高1/3以上的信噪比,增加了后期读取CAN总线故障码的准确率。     

基于小波包分解的意识脑电特征提取

针对2种不同意识任务(想象左手运动和想象右手运动)的脑-机接口(brain-computer interface,BCI)设计,提出了基于小波包分解的特征提取方法。首先深入研究了小波包变换,结合事件相关去同步化(event-related desynchronization,ERD)/事件相关同步化(event-related synchronization,ERS)现象,提出以小波包分解系数来考虑特征,然后对C3、C4导联脑电信号进行小波包分解系数方差和相对能量2种特征的提取,最后采用最简线性分类器进行分类。结果表明,2种特征对应的最大分类正确率均达到了85%,对应时间分别为4.34 s和4.39 s。因此,在保证分类正确率的前提下,所提方法更加简单和有效,为大脑意识任务分类提供了新思路。     

基于小波包分解的脑电信号特征提取

在脑机接口研究中,针对脑电信号的特征抽取,提出一种基于小波包分解的方法,利用Fisher距离准则,选择具有较大可分离性的特定子带小波包系数和能量作为有效特征,构成特征矢量,并采用BCI2003竞赛数据,通过对该特征矢量的可分性和识别精度2个指标的评估,表明了所提出方法的有效性。     

Fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition

This paper addresses the development of a random forest classifier for the multi-class fault diagnosis in spur gearboxes. The vibration signal’s condition parameters are first extracted by applying the wavelet packet decomposition with multiple mother wavelets, and the coefficients’ energy content for terminal nodes is used as the input feature for the classification problem. Then, a study through the parameters’ space to find the best values for the number of trees and the number of random features is performed. In this way, the best set of mother wavelets for the application is identified and the best features are selected through the internal ranking of the random forest classifier. The results show that the proposed method reached 98.68% in classification accuracy, and high efficiency and robustness in the models.     

基于小波包分解与主流形识别的非线性降噪

为解决工程实际中强噪声、非线性且频率成分复杂的振动信号降噪问题,提出了基于小波包分解和主流形识别的非线性降噪方法。采用小波包分解将原始振动信号正交无遗漏地分解到各频带范围内,根据各子频带中信噪空间分布,分别采用相应参数对小波包分解系数进行相空间重构;采用局部切空间排列(local tangent space alignment,LTSA)主流形识别方法在高维相空间中实现信号与噪音的分离,并重构出降噪后的一维小波包分解系数,最后进行小波包分解重构得到降噪后的振动信号。通过仿真实验和实例应用对本文所提方法的有效性进行了验证,试验结果表明本文方法具有良好的非线性降噪能力。     

Bearing performance degradation assessment based on lifting wavelet packet decomposition and fuzzy c-means

Bearing performance degradation assessment is crucial to realize condition-based maintenance. In this paper, a new method for it is proposed based on lifting wavelet packet decomposition and fuzzy c-means. Feature vectors are composed of the node energies of lifting wavelet packet decomposition. Normal and final failure data are used as training samples to build assessment model utilizing fuzzy c-means, and the subjection of tested data to normal state is defined as the degradation indicator, which has intuitionistics explanation related to degradation degree. Results of its application to accelerated bearing life test show that this indicator can reflect effectively performance degradation of bearing. And after discussing the influence of outliers in training set, a robust strategy is proposed.     

De-noising of diesel vibration signal using wavelet packet and singular value decomposition

The vibration signals of diesel include excess noise that must be eliminated before extraction of characteristic parameters. Firstly, the effects of vibration-signal de-noising among Fourier transform, wavelet decomposition and wavelet packet decomposition are compared. Secondly, singular value decomposition is applied to de-noising vibration signals. Finally, a new de-noise method integrated with wavelet packet and singular value is presented. In this method, vibration signals are decomposed by wavelet packet, and the wavelet packet coefficient is de-noised by singular value decomposition again. The results indicate that the new de-noising method is the best. The SNR (signal-to-noise ratio) of the vibration signals of a diesel cylinder lid is the highest. The diesel vibration waveforms of combustion and valve become clear and the extracted characteristic parameters become more precise. __________ Translated from Journal of China University of Petroleum (Natural Science Edition), 2006, 30(1) (in Chinese)     

Prognostics for drilling process with wavelet packet decomposition

Automation solutions ensure determinism and reproducibility for the handling and aligning of work pieces and tools in micro- and ultra-precision technologies. Automation in this context means the handling and alignment of parts and tools within the entire process chain to achieve adjustment and alignment accuracies at a level well below 0.5 μm. The exact knowledge about the position and the condition of the work pieces and tools throughout the entire process chain is the key issue in the automated production chain. This knowledge enables the exact referencing of the work piece within the machine tool coordinate system and an offset compensation by the machine tool axes as well as by active work piece clamping devices. The automation solutions enable a cost-effective, ultra-high quality production technology for the achievement of nanometre form accuracies and super smooth surface finishes. These are required for ultra-precise components in biomedical-, sensor- as well as consumer goods applications and are revolutionary throughout the world's technologies.