基于小波子带分解的特征参数对语音自动切分的改进

采用了基于小波子带分解的特征提取方法,根据DCT和DWT两种去相关方法的不同,得到语音信号的特征参数分别为SubbandBasedCepstral(SBC)和WaveletPacketParameters(WPP)。实验切分结果表明,基于小波子带分解的特征参数比MFCC取得更好的切分效果。     

Emotion modeling from speech signal based on wavelet packet transform

The recognition of emotion in human speech has gained increasing attention in recent years due to the wide variety of applications that benefit from such technology. Detecting emotion from speech can be viewed as a classification task. It consists of assigning, out of a fixed set, an emotion category e.g. happiness, anger, to a speech utterance. In this paper, we have tackled two emotions namely happiness and anger. The parameters extracted from speech signal depend on speaker, spoken word as well as emotion. To detect the emotion, we have kept the spoken utterance and the speaker constant and only the emotion is changed. Different features are extracted to identify the parameters responsible for emotion. Wavelet packet transform (WPT) is found to be emotion specific. We have performed the experiments using three methods. Method uses WPT and compares the number of coefficients greater than threshold in different bands. Second method uses energy ratios of different bands using WPT and compares the energy ratios in different bands. The third method is a conventional method using MFCC. The results obtained using WPT for angry, happy and neutral mode are 85 %, 65 % and 80 % respectively as compared to results obtained using MFCC i.e. 75 %, 45 % and 60 % respectively for the three emotions. Based on WPT features a model is proposed for emotion conversion namely neutral to angry and neutral to happy emotion.     

小波变换的语音去噪方法

提出一种改进的语音去噪处理方法：二次小波分解全局阈值法。该方法不同于传统阈值滤波方法,首先对语音信号的高频部分进行二次分解,然后应用阈值滤波的方法对信号进行去噪处理。该方法在MATLAB上进行了模拟实验,实验结果表明该方法提高了信噪比,去除了大部分噪声,相当完整地保留了有效信号能量,很好地解决噪声对语音信号干扰的问题。     

一种时间自适应阈值的小波包改进语音增强算法

针对传统小波语音增强算法存在过度周值处理的问题,提出一种改进的时间自适应阈值小波包去噪算法.该方法采用听觉感知小波包对噪声语音进行分解,得到小波包听觉感知节点上的系数,并基于语音存在概率估计按帧自动调节去噪周值,因改进的闲值能更好地避免语音小波包系数被过度阈值处理的情况,从而在抑制噪声的同时保留了更多的原始语音成分,进一步提高了降噪效果,实验结果表明,该算法比常规小波自适应闻值算法能得到更清晰的语音增强信号.     

基于小波及能量熵的带噪语音端点检测算法

为了在复杂的噪声环境中区分出语音信号和非语音信号(噪声),提出了一种基于小波及能量熵的带噪语音端点检测方法.该方法利用小波的多分辨率特性以及它对非平稳信号局部特征的表现能力,对含噪语音信号进行小波变换,用各层能量熵值的平均值来有效地区分语音段和非语音段.不同背景噪声及不同信噪比下的实验结果表明,提出的带噪语音端点检测算法获得了较高的检测正确率.     

Speaker identification using discrete wavelet packet transform technique with irregular decomposition

This paper presents the study of speaker identification for security systems based on the energy of speaker utterances. The proposed system consisted of a combination of signal pre-process, feature extraction using wavelet packet transform (WPT) and speaker identification using artificial neural network. In the signal pre-process, the amplitude of utterances, for a same sentence, were normalized for preventing an error estimation caused by speakers’ change in volume. In the feature extraction, three conventional methods were considered in the experiments and compared with the irregular decomposition method in the proposed system. In order to verify the effect of the proposed system for identification, a general regressive neural network (GRNN) was used and compared in the experimental investigation. The experimental results demonstrated the effectiveness of the proposed speaker identification system and were compared with the discrete wavelet transform (DWT), conventional WPT and WPT in Mel scale.     

脑电信号的小波变换和样本熵特征提取方法

针对现有的采用单一的特征提取算法对运动想象脑电信号识别率不高的问题,提出一种结合小波变换和样本熵的特征提取方法.通过小波变换,把脑电信号进行3层分解,抽取出对应于脑电β节律频带的小波系数的能量均值和能量均值差,并结合脑电信号的样本熵组成特征向量,使用支持向量机分类器对左右手运动想象脑电信号进行分类.结果表明,结合小波变换和样本熵的特征提取方法明显优于仅采用小波变换、样本熵以及其他传统的特征提取方法,得到的最高正确识别率为91.43%.     

基于改进小波包与样本熵的表面肌电信号特征提取

针对表面肌电信号(sEMG)进行小波包分解后子空间维数较大导致部分子空间的特征信息被减弱和相邻子空间的频率接近会导致不同程度的信息冗余问题,提出了一种基于改进小波包与样本熵相结合的特征提取方法.在对原始sEMG进行小波变换的同时,对其某一高频子空间也进行小波变换,提取改进小波包分解后四个低频子空间的样本熵作为特征.在智能轮椅平台上进行实验,实验结果显示采用基于该算法的轮椅系统不仅正确识别率较高,而且稳定性更好.     

基于修正维纳滤波的小波包变换图像去噪

图像去噪是图像处理中一个非常重要的环节。为了改善降质图像质量,根据Donoho提出的小波阈值去噪算法,分析了维纳滤波原理,提出了一种基于修正维纳滤波的小波包变换图像去噪方法。利用修正维纳滤波对噪声图像进行处理,用处理后的图像计算噪声的标准方差,以此作为小波包的阈值。利用小波包对维纳滤波后的图像进行分解,实现对图像的低频和高频部分分别进行分解,用计算出的阈值对小波包树系数进行软阈值处理。利用小波包逆变换来获取去噪后的图像。结果表明：在噪声方差为0.01时,经该算法去噪后图像的PSNR比小波包自适应阈值去噪后的PSNR高出8.8 dB。该算法不仅能有效地去除加性高斯白噪声,而且能很好地保留边缘信息,极大地改善了图像的视觉质量。     

基于小波变换和样本熵的假手肌电模式识别方法

提出一种新的模式分类器,利用安置在拇长屈肌、指深屈肌和指伸肌上的3个电极所测得的肌电信号,实现了对3自由度假手手指运动的控制．该分类器采用小波变换和样本熵的方法构造特征矢量．经过由变学习速率算法和RP算法构建的集成3层前馈神经网络的分类,能够成功地分辨出拇指、食指和中指的弯曲与伸展运动,平均识别率可达96％以上．实验结果表明,该分类器为多自由度肌电假手的控制提供了一种有效的方法．     

采用局部场电位小波包熵分析空间频率特性

空间频率是视觉刺激的基本特征之一,为了研究视觉皮层神经元对刺激空间频率的响应特性,提出了一种基于局部场电位小波包熵的分析方法。通过以Long Evans大鼠为模式动物进行电生理实验,分别采用神经元放电统计分析和局部场电位小波包熵分析,发现不同空间频率刺激下,小波包熵调谐曲线与全局神经元放电调谐曲线具有一致性,证明了局部场电位小波包熵可用于表征视皮层神经元对刺激空间频率的选择性。结果还表明采用基于局部场电位小波包熵分析时,各通道结果具有更好的一致性。     

小波包变换与Teager能量算子结合的说话人识别

在说话人识别系统中,语音特征参数的提取是影响系统性能的关键因素之一。在研究了MFCC参数的基础上,结合MFCC参数在信号的低频部分具有高频率分辨率以及小波包变换可以对信号的高频部分进行分解以提高高频部分的频率分辨率的优点,将二者结合,将Teager能量算子引入到信号高频部分的能量参数求解,构造了一种新的混合特征参数,采用支持向量机实现说话人的分类识别。实验结果表明,该特征参数有效提高了说话人辨识系统的识别率。     

复小波包变换域混合统计模型图像降噪算法

该方法利用四树复小波包变换具有的移不变性、良好的方向选择性和对高频信号的细致分析能力等特点, 把含噪图像分解成低频逼近子图和若干高频方向子图; 在保留低频逼近子图复系数不变的同时, 利用复系数层间相关性的强弱把高频方向子图分为主要类和次要类. 对主要类和次要类复系数分别进一步采用非高斯双变量模型和零均值高斯分布模型进行噪声抑制. 实验结果表明, 无论是峰值信噪比(PSNR)指标, 还是在视觉效果上, 本文方 法的去噪性能均好于传统的双树复小波变换去噪、四树复小波包变换去噪和小波域高斯尺度混合模型去噪, 在有效抑制噪声的同时, 具有很好的图像边缘和细节保护能力.     

Classification of speech dysfluencies with MFCC and LPCC features

The goal of this paper is to discuss comparison of speech parameterization methods: Mel-Frequency Cepstrum Coefficients (MFCC) and Linear Prediction Cepstrum Coefficients (LPCC) for recognizing the stuttered events. Speech samples from UCLASS are used for our analysis. The stuttered events are identified through manual segmentation and used for feature extraction. Two simple classifiers are used for testing the proposed features. Conventional validation method is used for testing the reliability of the classifier. The experimental investigation elucidates MFCC and LPCC features which can be used for identifying the stuttered events and LPCC features were slightly outperformed than MFCC features.     

基于小波包分解的 SAR图像压缩 *

针对 SAR图像含有丰富的中、高频信息 ,而基于小波变换的图像压缩方法会丢失高频细节信息 ,提出了基于小波包分解的 SAR图像编码算法。小波包变换对 SAR图像进行完全分解 ,再用与后续编码器相关联的代价函数进行最佳基搜索 ,然后根据各子带小波包系数的重要性进行加权 ,采用多级树集合分裂算法 ( SPIHT)编码。实验结果表明 ,该算法更好地保留了 SAR图像的细节信息 ,获得了同压缩比下优于传统 SPIHT算法的编码性能 ,更有利于后续图像处理。     

Spur bevel gearbox fault diagnosis using wavelet packet transform and rough set theory

The gearbox is an important component in industrial drives, providing safe and reliable operation for industrial production. Wavelet packet transform (WPT) analysis was used to extract fault features in the vibration signals generated by a gearbox. The extracted features from the WPT were used as input in a rough set (RS) for attribute reduction and then combined with a genetic algorithm to obtain global optimal attribute reduction results. The fault features gained after the attribute reductions were used to generate decision rules. The unknown gear status signal attributes were used as input to match the generated decision rules for fault diagnosis purposes. Gearbox vibration signals contain a significant amount of gear status information; a WPT has an acute portion-locked ability to extract attribute information from the vibration signals. However, WPT frequency aliasing would lead to the generation of spurious frequency components, affecting gear fault diagnosis. In this paper, we introduce an improved WPT to eliminate frequency aliasing, thus improving the accuracy of fault diagnosis. This paper studies the use of wavelet packet for feature extraction and the RS for classification; the results demonstrate that this method can accurately and reliably detect failure modes in a gearbox.     

一种新阈值函数的小波包语音增强算法

针对传统软、硬阈值函数去噪方法增强的语音存在失真的问题,提出一种新阈值函数的小波包语音增强算法,同时给出了新阈值函数和新的Bark尺度小波包分解结构。新阈值函数在小波包系数绝对值大于给定阈值的区间内,灵活地结合了软、硬阈值函数;在小波包系数绝对值小于给定阈值的区间内,用一种非线性函数代替传统阈值函数中的简单置零,实现了阈值函数的平缓过渡;新的60个频带Bark尺度小波包分解结构能更好地模拟人耳的听觉感知特性。仿真实验结果表明,在高斯白噪声和有色噪声背景下,与传统软、硬阈值函数去噪方法相比,新算法有效提高了增强语音信噪比和分段信噪比,减少了语音失真,具有更好的去噪效果。     

小波包特征熵分解的图像水印算法

提出了一种小波包特征熵分解的自适应图像水印算法。该算法通过对宿主图像进行小波包特征熵分解,在高频系数中选取合适的阈值来区分高频系数中图像的纹理细节信息和噪声信息,将水印信息自适应地嵌入到高频系数当中。实验表明,该水印算法对噪声、JPEG2000压缩、滤波、改变对比度、几何剪切等攻击都具有一定的健壮性。     

Comparison of wavelet families for texture classification by using wavelet packet entropy adaptive network based fuzzy inference system

Recently, significant of the robust texture image classification has increased. The texture image classification is used for many areas such as medicine image processing, radar image processing, etc. In this study, a new method for invariant pixel regions texture image classification is presented. Wavelet packet entropy adaptive network based fuzzy inference system (WPEANFIS) was developed for classification of the twenty 512 × 512 texture images obtained from Brodatz image album. There, sixty 32 × 32 image regions were randomly selected (overlapping or non-overlapping) from each of these 20 images. Thirty of these image regions and other 30 of these image regions are used for training and testing processing of the WPEANFIS, respectively. In this application study, Daubechies, biorthogonal, coiflets, and symlets wavelet families were used for wavelet packet transform part of the WPEANFIS algorithm, respectively. In this way, effects to correct texture classification performance of these wavelet families were compared. Efficiency of WPEANFIS developed method was tested and a mean %93.12 recognition success was obtained.     

NSCT变换和小波包变换相结合的图像隐藏方法

为了更好地解决NSCT域图像隐藏不可见性和鲁棒性之间的矛盾,提出了一种基于NSCT变换和小波包变换相结合的可见光图像隐藏方法,利用NSCT变换将载体图像分解为低频子带和一组高频子带,对低频子带进行二级小波包分解,通过奇异值变换将秘密图像重要位平面信息隐藏在小波包分解低频子带中,次要信息自适应隐藏在NSCT高频子带中。实验表明,在同等嵌入容量下,算法峰值信噪比大于50 dB,对几何攻击和滤波等干扰处理后,秘密图像的归一化系数仍接近于1。