一种用于地震信号分析的二阶挤压小波变换算法

地震信号分析在地质岩性、储层、流体、沉积相带的检测,以及地层界面识别与储层分析、地震资料处理和解释等方面具有重要研究意义。针对现有时频分析算法在处理地震信号时,存在时频分辨率低、能量聚集性差等问题,该文以Ricker子波为数学模型,提出了一种新的2阶挤压小波变换算法(SWT2)。考虑到传统时频同步压缩变换中的瞬时频率估计对地震信号失效,利用改进的母小波对地震信号进行匹配,进而通过谱峰对齐对参考频率进行修正,从而提升时频能量聚集性和时频分辨率。仿真实验结果表明,提出的2阶挤压小波变换算法可以极大地提升地震信号的时频聚集性,精确地反映信号的时延和主频,对地层结构的刻画更加精确。     

小波与宽带相关处理

小波与宽带相关处理Ｌ·Ｇ．威斯虽然小波已被数学家们研究了许多年．但在工程方面获得大量应用还是近十年的事。这种复话是由Ｍｏｒｌｅｔ在地球勘探研究中开始的，此后，Ｇｒｏｓｓｍａｎ进行了详细的数学分析。小波已成功地应用在许多信号处理领域，如图象处理、语音处...     

四种时频分析方法的频率分辨率研究

时频分析是分析时变谱的有力工具,其频率分辨率是值得研究的关键问题之一。通过对基于短时傅里叶变换、小波变换、Choi-Williams分布和Hilbert-Huang变换的四种时频分析方法的频率分辨率的实验比较,说明Hilbert-Huang变换法具有最高的频率分辨率,其次是Choi-Williams分布,小波紧随其后,最差是短时傅里叶变换。     

时频分布的分辨率比较

利用时频聚集性较好的高斯函数作为标准信号,分析了短时傅里叶变换、小波变换、谱图、尺度图、Wigner-Ville分布的分辨率以及交叉项对分辨率的影响,并对它们的分辨率进行了比较;为了进行时间-尺度分布与时频分布的比较,提出了改进小波变换;在分辨率比较中,给出了几种典型情况的模拟结果.     

复解析小波变换与语音信号包络提取和分析

利用传统的Ｈｉｌｂｅｒｔ变换方法提取语音信号包络存在一些固有缺陷,为此本文建议了一种复解析小波变换包络提取新方法。该方法将Ｈｉｌｂｅｒｔ分析与小波分析紧密地结合在一起。文中推导并论述了新包络滤波器的时域、频域构造条件和设计方法,选择Ｍｏｒｌｅｔ复小波对语音信号进行了初步数字仿真实验,结果证实了理论分析的正确性,并显示出文中提出的新的分析方法对于信号处理上有许多独特优势和潜在性能。     

基于发动机冷试的非平稳信号分析与特征提取

联合时频是分析非平稳信号的有力工具,文章通过几种时频分析如STFT、Wigner-Ville分布、小波变换和小波能量商,对发动机冷试振动信号进行分析。结果表明:STFT、WV、及WT均能一定程度反应信号的时频分布,STFT频率分辨率有限,WV分布存在一定的交叉干扰项,小波变换能够在各个尺度对信号进行观察,小波包能量商能够清楚地观察信号的能量分布,能够作为发动机状态的特征向量。     

基于时频谱融合的加速度计信号分析

时频谱分析可以提供信号在时间域和频率域的联合分布信息,针对该方法很难同时保证较高的时间分辨率和频率分辨率的问题,提出了一种时频聚集性很高的谱融合方法。先用谐波小波包将信号分解到不同频段,再用高时间分辨率的Morlet小波和高频率分辨率的短时傅里叶变换分别对各个频段上的分量进行分析,得到小波尺度矩阵和短时傅里叶变换时频矩阵,然后通过算法将二者融合在一个时频谱中。通过仿真和对动态条件下加速度计信号的分析,证明该算法既能提取出微弱的动态变化,又具有较高的时频分辨率,可以直观、全面、精确地对信号进行识别。     

小波包分解对心理声学模型的改进

为了改变常用心理声学模型中均匀谱分析造成的时频分辨率不足的问题,采用小波包分解对信号进行分析.通过Matlab对信号进行小波包分解处理,代替常用心理声学模型的FFT,改善了时频分辨率不足的问题,且通过小波包分解得到的频带划分.比常用心理声学模型得到的频带划分更接近于人耳的临界频带,更适应于人耳的听觉特性.     

一种基于小波变换和隐Markov模型的声调识别方法

本文给出了一种基于小波变换和隐Ｍａａｒｋｏｖ模型的声调识别方法，根据小波变换检测信号突变的性质，充分利用多分辨率分析，准确可靠地实现了基音检测；采用分划Ｇａｕｓｓ混合概率密度函数的ＨＭＭ进行汉语声调识别，推导出用ＰＧＭ函数的Ｖｉｔｅｒｂｉ长法的简化递推式。     

基于先验估计的自适应Chirplet信号展开

该文提出一种新的时频表示方法--自适应线性调频小波(Chirplet)信号展开算法。算法基于信号本征空间,融参数的初值估计和精确估计于一体,利用匹配追踪算法将信号自适应地展开在高斯线性调频小波基函数集上。通过展开系数和基函数参数获得信号的时频分布,其时频聚集性、抗噪性和时频分辨率不仅优于一般的时频分布而且优于已有的自适应时频分布,可以更好地刻画信号的本质。应用数值仿真检验了算法的有效性和时频分布的优良性能。     

A Capon's time-octave representation application in room acoustics

In time-frequency analysis, Capon's estimator has proven its efficiency in precise applications. In a context where a time-octave representation is also necessary, the authors propose a new method combining both Capon's estimator and a time-octave representation. The main objective is to obtain legibility in the time frequency plane using a variable frequency resolution with a fixed time resolution. This fixed time resolution is possible owing to the good resolution properties of Capon's estimator compared to the Fourier transform. This choice leads to a particular repartition of basic cells in the time-frequency plane that seems more adapted to a physical interpretation in the application presented. Nevertheless, a parallel with the wavelet transform is displayed: the constructed wavelet is adapted to the signal at each octave or at each fraction of octave. The proposed method is presented both in continuous and discrete formulations. Its structure is studied and a simplification is proposed when precise hypotheses are verified. Simulations and comparisons with classical representations (spectrogram, scalogram) are discussed. The contribution of each method, essentially in the duality of time-frequency and time-scale, are shown up in relation to the analyzed signal. Lastly, the proposed method and classical ones are applied on rear signals issued from room acoustics where the aim is the time-frequency characterization of concert halls from impulse responses     

利用小波变换实现基于结构光投影的S变换轮廓术

S变换是一种集合了窗口傅里叶变换和小波变换优点的时-频分析技术,将一维的信号映射到二维的时-频空间,具有良好的时频分辨能力。由于S变换谱和傅里叶变换频谱之间存在直接联系,且具有类似于小波变换的多分辨率能力,S变换可以通过快速傅里叶变换算法实现,也可以通过小波变换算法实现。研究了基于小波变换算法的S变换在基于结构光投影的三维光学测量中的应用,给出了理论分析,特别讨论了S变换中频率因子的选择,并同基于快速傅里叶变换算法的S变换轮廓术结果进行了比较。完成了计算机模拟和实验研究。     

一种基于多窗宽度STFT的PSWFs信号时频分析方法

固定窗宽度的短时傅里叶变换(Short Time Fourier Transform,STFT)时频分辨率是固定的,难以对频率时变的椭圆球面波函数(Prolate Spheroidal Wave Functions,PSWFs)信号的时频分布特性进行全面分析.剖析了窗宽度对不同参数PSWFs信号STFT时频分布的影响,...     

The wavelet transform, time-frequency localization and signalanalysis

Two different procedures for effecting a frequency analysis of a time-dependent signal locally in time are studied. The first procedure is the short-time or windowed Fourier transform; the second is the wavelet transform, in which high-frequency components are studied with sharper time resolution than low-frequency components. The similarities and the differences between these two methods are discussed. For both schemes a detailed study is made of the reconstruction method and its stability as a function of the chosen time-frequency density. Finally, the notion of time-frequency localization is made precise, within this framework, by two localization theorems     

喘鸣音的时频谱图特征提取与信号检测

文中提出了一种基于S变换时频谱图的喘鸣音信号检测算法。喘鸣音在时域中具有类似正弦波的形态,但难以直接提取特征。S变换在高频处具有较高的时间分辨率,在低频处具有较高的频率分辨率,可精细化分析喘鸣音信号时频特征。文中通过对呼吸音信号做S变换生成对应的时频谱图,提取与喘鸣音对应的二维谱图像特征,实现了喘鸣音信号检测。实验表明该算法对单个体自身训练的情形检测效果理想,检测敏感性指标可达100%,正阳性预测值可达98%以上。但对于喘鸣音共性特征提取欠缺,有待进一步探索。      

Wavelets and time-frequency analysis

We present a selective overview of time-frequency analysis and some of its key problems. In particular we motivate the introduction of wavelet and wavelet packet analysis. Different types of decompositions of an idealized time-frequency plane provide the basis for understanding the performance of the numerical algorithms and their corresponding interpretations within the continuous models. As examples we show how to control the frequency spreading of wavelet packets at high frequencies using nonstationary filtering and study some properties of periodic wavelet packets. Furthermore we derive a formula to compute the time localization of a wavelet packet from its indexes which is exact for linear phase filters, and show how this estimate deteriorates with deviation from linear phase     

Bionic wavelet transform: a new time-frequency method based on an auditory model

In this paper, a new adaptive wavelet transform, named bionic wavelet transform (BWT), is developed based on a model of the active auditory system. The most distinguishing characteristic of BWT is that its resolution in the time-frequency domain can be adaptively adjusted not only by the signal frequency but also by the signal instantaneous amplitude and its first-order differential. The automatically adjusted resolution, even in a fixed frequency along the time-axis, is achieved by introducing the active control of the auditory system into the wavelet transform (WT). Other properties of BWT include that: 1) BWT is a nonlinear transform that has high sensitivity and frequency selectivity; 2) BWT represents the signal with a concentrated energy distribution; and 3) the inverse BWT can reconstruct the original signal from its time-frequency representation. In order to compare these three properties between BWT and WT, experiments were conducted on both constructed signals and real speech signals. The results show that BWT performs better than WT in these three aspects, and that BWT is appropriate for speech signal processing, especially for cochlear implants.     

基于最优小波基函数的语音增强研究

小波分析具有时频局部化和多分辨率特性,而小波包分析是在小波分析的基础上对信号高频部分的更精细地分解,选取合适的小波基一直是小波包去噪分析中的关键问题。将熵函数作为选取最优小波基的评价标准,通过计算语音信号小波包分解系数的熵值来确定合适的分解方式,同时采用小波包阈值去噪算法对三种小波基进行小波包去噪仿真实验,并进行对比分析。仿真实验表明,两种熵函数选取的最优小波基都能较好地消除强噪声背景下的噪声,得到信噪比较高的语音信号。     

基于最优小波包基的信号增强算法研究及应用

本文介绍了小波包分析的基本理论以及小波包信号降噪的基本原理,与小波变换相比,小波包变换则是对小波分解中所得到的高频部分再继续细分为一些子频带,具有更精细的信噪分离能力,所以对包含大量中、高频信息的信号能更好地进行时频局部化分析。小波包变换在信号去噪中有着非常重要的应用,因此利用小波包对信号进行消噪也越来越受到科学界的关注。本文的主旨在于研究最优小波包基函数的选取方法,以小波包分析为基础,根据最小代价原理研究信号分解的最佳小波包基,从而在最优小波包基的基础上获得最好的信号增强效果。