病态嗓音特征的小波变换提取及识别研究

通过分析嗓音的发音机理、病态嗓音与正常嗓音在频域的表现差异,利用小波变换对信号进行分解,突出病态嗓音的特点,提出了基于多尺度分析的小波降噪、分解的熵系数(Entropy Coefficient based on De-noise,Decomposition of Multi-scale Analysis,ECDDMA)作为识别的特征矢量集。并对比分析了语音识别中经典特征参数Mel倒谱系数(MFCC),分别运用这两种特征参数对242例正常嗓音和234例病态嗓音运用高斯混合模型(GMM)进行了识别。结果显示:ECDDMA系数较传统的模拟人耳听觉非线性特性的MFCC及其动态特征能更准确地表征正常与病态嗓音之间的差异,有利于同时提高病态和正常嗓音的识别率。     

基于局部线性逼近的流形学习算法

流形学习方法是根据流形的定义提出的一种非线性数据降维方法,主要思想是发现嵌入在高维数据空间的低维光滑流形.局部线性嵌入算法是应用比较广泛的一种流形学习方法,传统的局部线性嵌入算法的一个主要缺点就是在处理稀疏源数据时会失效,而实际应用中很多情况还要面对处理源数据稀疏的问题.在分析局部线性嵌入算法的基础上提出了基于局部线性逼近思想的流形学习算法,其通过采用直接估计梯度值的方法达到局部线性逼近的目的,从而实现高维非线性数据的维数约简,最后在S-曲线上进行稀疏采样测试取得良好降维效果.     

一种在源数据稀疏情况下的数据降维算法

流形学习方法是根据流形的定义提出的一种非线性数据降维方法,主要思想是发现嵌入在高维数据空间的低维光滑流形。从分析基于流形学习理论的局部线性嵌入算法入手,针对传统的局部线性嵌入算法在源数据稀疏时会失效的缺点,提出了基于局部线性逼近思想的流形学习算法,并在S-曲线上采样测试取得良好降维效果。     

病态嗓音的识别与研究

通过分析嗓音的发音机理,提取正常与病态嗓音的传统声学参数：基频、共振峰、Mel倒谱系数（MFCC）,以及非线性特征参数：计盒维数与截距,作为病态嗓音识别的特征矢量集。应用高斯混合模型（GMM）对156例正常嗓音与146例病态嗓音进行建模与识别。结果表明：非线性特征参数计盒维数与截距能很好地区分正常与病态嗓音,它们与传统声学参数基频和共振峰的组合,能够取得92.60%的识别率。     

基于直接估计梯度思想的数据降维算法

高维非线性数据的降维处理对于计算机完成高复杂度的数据源分析是非常重要的。从拓扑学角度分析,维数约简的过程是挖掘嵌入在高维数据中的低维线性或非线性的流形。该文在局部嵌入思想的流形学习算法的基础上,提出直接估计梯度值的方法,从而达到局部线性误差逼近最小化,实现高维非线性数据的维数约简,并在Swiss roll曲线上采样测试取得了良好的降维效果。     

自适应局部线性嵌入算法

局部线性嵌入算法（LLE）因其较低的计算复杂度和高效性适用于很多降维问题,新的自适应局部线性嵌入（ALLE）算法对数据进行非线性降维,提取高维数据的本质特征,并保持了数据的全局几何结构特征,对比实验结果表明了该算法对于非理想数据的降维结果均优于LLE算法。     

基于流形学习的维数约简算法

介绍线性维数约简的主成分分析和多维尺度算法,描述几种经典的能发现嵌入在高维数据空间的低维光滑流形非线性维数约简算法,包括等距映射、局部线性嵌入、拉普拉斯特征映射、局部切空间排列、最大方差展开。与线性维数约简算法相比,非线性维数约简算法通过维数约简能够发现不同类型非线性高维数据的本质特征。     

基于自适应近邻参数的局部线性嵌入

局部线性嵌入算法是一种有效的非线性降维方法。文中提出一种自适应的局部线性嵌入方法。该方法通过分析数据集中任意样本所在局部区域的线性重构误差,确定该局部区域的近似线性块,然后根据位于此局部线性块上的样本来选择局部线性嵌入的近邻参数。实验结果表明,在不同的数据集上,采用多个评价标准,自适应的局部线性嵌入方法相比普通的局部线性嵌入方法,取得更好的结果。     

一种基于潜在语义线性嵌入的三维模型检索方法

在三维模型检索中,为了保持模型之间的形状相似性,并且考虑保持模型之间的语义关系不变的情况下实现特征的维数缩减,提出一个基于潜在语义分析的非线性降维方法潜在语义线性嵌入LSLE.该方法通过构建的三维模型语义空间确定数据库中三维模型的语义近邻,并通过改进的局部线性嵌入方法实现数据维数的缩减,对Princeton Shape Benchmark的实验表明,该方法在三维模型的特征降维和检索中都取得了较好的效果.     

基于组合方法的人脸识别算法研究

该文试探一种加权融合流形学习的方法进行人脸识别,该算法通过Haar小波和局部线性嵌入(LLE)加权融合的方式来进行人脸识别。首先通过Haar小波变换对人脸图像进行一级分解,得到四个子图;然后利用LLE算法对四个子图降维处理,并加权融合;最后利用支持向量机(SVM)进行分类判决。通过在ORL库中进行实验,结果表明,该文提出的Haar+LLE识别效率比单独使用主成分分析(PCA)和LLE更高效。     

Dimensionality reduction-based spoken emotion recognition

To improve effectively the performance on spoken emotion recognition, it is needed to perform nonlinear dimensionality reduction for speech data lying on a nonlinear manifold embedded in a high-dimensional acoustic space. In this paper, a new supervised manifold learning algorithm for nonlinear dimensionality reduction, called modified supervised locally linear embedding algorithm (MSLLE) is proposed for spoken emotion recognition. MSLLE aims at enlarging the interclass distance while shrinking the intraclass distance in an effort to promote the discriminating power and generalization ability of low-dimensional embedded data representations. To compare the performance of MSLLE, not only three unsupervised dimensionality reduction methods, i.e., principal component analysis (PCA), locally linear embedding (LLE) and isometric mapping (Isomap), but also five supervised dimensionality reduction methods, i.e., linear discriminant analysis (LDA), supervised locally linear embedding (SLLE), local Fisher discriminant analysis (LFDA), neighborhood component analysis (NCA) and maximally collapsing metric learning (MCML), are used to perform dimensionality reduction on spoken emotion recognition tasks. Experimental results on two emotional speech databases, i.e. the spontaneous Chinese database and the acted Berlin database, confirm the validity and promising performance of the proposed method.     

基于局部线性嵌入算法的柴油机故障诊断研究

为提高柴油机故障诊断准确率和效率,提出了改进局部线性嵌入算法的柴油机诊断系统。应用小波包能量谱分析方法提取某柴油机振动信号的特征值,将提取的高维特征向量映射到低维空间上,能将高维特征向量进行优化,即特征值的二次提取。该改进算法可模糊化近邻点k的选择,从而提高计算的速度,并应用SOM-BP神经网络进行故障识别。实验表明,经过局部线性嵌入算法的特征值优化,能减少SOM-BP神经网络的输入节点,可在一定程度上提高故障识别的效率和准确率。     

基于特征点融合小波能量特征的手背静脉识别

手背静脉识别是生物识别领域的新兴课题,针对单一手背静脉识别方法在大量样本情况下正确识别率识较低的问题,提出了一种空间域特征融合小波域特征的识别方法,对预处理后的样本提取了空间域中的特征点以及在小波域中构造了小波能特征,并分别用改进的豪斯多夫距离以及加权城区距离进行度量,最后将两种方法进行加权融合,采用最近邻分类器进行识别;在具有100个样本的数据库上对该方法进行了测试,在最近邻分类阈值为9.46时识别率达到97.2%,表明了该方法的优越性.     

Locally Minimizing Embedding and Globally Maximizing Variance: Unsupervised Linear Difference Projection for Dimensionality Reduction

Recently, many dimensionality reduction algorithms, including local methods and global methods, have been presented. The representative local linear methods are locally linear embedding (LLE) and linear preserving projections (LPP), which seek to find an embedding space that preserves local information to explore the intrinsic characteristics of high dimensional data. However, both of them still fail to nicely deal with the sparsely sampled or noise contaminated datasets, where the local neighborhood structure is critically distorted. On the contrary, principal component analysis (PCA), the most frequently used global method, preserves the total variance by maximizing the trace of feature variance matrix. But PCA cannot preserve local information due to pursuing maximal variance. In order to integrate the locality and globality together and avoid the drawback in LLE and PCA, in this paper, inspired by the dimensionality reduction methods of LLE and PCA, we propose a new dimensionality reduction method for face recognition, namely, unsupervised linear difference projection (ULDP). This approach can be regarded as the integration of a local approach (LLE) and a global approach (PCA), so that it has better performance and robustness in applications. Experimental results on the ORL, YALE and AR face databases show the effectiveness of the proposed method on face recognition.     

基于改进MFCC融合特征及FA-PNN的驾驶员路怒情绪识别

现今关于驾驶员路怒情绪识别方法中语音特性分析相对较少,该研究以路怒情绪为研究对象,利用模拟驾驶系统建立数据集,通过分析驾驶员语音的频谱特征,将时域中短时能量及短时过零率特征参数和改进Mel频率倒谱系数（Mel frequency cepstral coefficients,MFCC）特征参数融合构成特征参数向量,利用萤火虫算法（firefly algorithm,FA）优化PNN神经网络（probabilistic neural networks）并构建识别模型,实现驾驶员路怒情绪的识别。实验结果表明,在相同神经网络下,改进MFCC融合特征提取方法相比传统MFCC特征提取方法具有更好的抗噪性。同时,FA-PNN模型的识别准确率为93.0%,相比传统PNN模型提高了11个百分点;F1-Score值为0.932 8,提高了0.104 7。该研究论证了语音信号处理技术对驾驶员路怒情绪识别的可行性,为汽车主动安全驾驶预警研究提供了新方法。     

基于非线性降维的水下目标融合识别决策

针对以传统的谱图和模型参教等方法提取得到的水下目标特征高维数据,文章提出了LPP的非线性降维方法,有效的去除特征向量各分量间的相关性,增大了特征向量的可分性,降低了后期识别的计算复杂度。同时设计了DS-SVM融合决策分类器对获得的低堆特征向量进行分类识别。与传统的SVM算法的分类识别效果进行比较分析,所得结果表明该方法有效的提高了水下目标识别的准确性,是一种行之有效的方法。     

基于Gabor小波与深度信念网络的人脸识别方法

特征提取与模式分类是人脸识别的两个关键问题。针对人脸识别中的高维和小样本问题,从人脸特征的提取与降维算法入手,提出基于受限玻尔兹曼机(RBM)的二次特征提取及降维算法模型。首先把图像均匀分成若干局部图像块并进行量化,再对图像进行Gabor小波变换,通过RBM对得到的Gabor人脸特征进行编码,学习数据更本质的特征,从而达到对高维人脸特征降维的目的;并以此为基础提出基于深度信念网络(DBN)的多通道人脸识别算法。在ORL、UMIST和FERET人脸库上对不同样本规模和不同分辨率的图像进行实验,识别结果表明,与采用线性降维和浅层网络的方法相比,所提方法取得了较好的学习效率和很好的识别效果。     

一种基于局部流形结构的无监督特征学习方法(英文)

Unsupervised feature selection is fundamental in statistical pattern recognition, and has drawn persistent attention in the past several decades. Recently, much work has shown that feature selection can be formulated as nonlinear dimensionality reduction with discrete constraints. This line of research emphasizes utilizing the manifold learning techniques, where feature selection and learning can be studied based on the manifold assumption in data distribution. Many existing feature selection methods such as Laplacian score, SPEC(spectrum decomposition of graph Laplacian), TR(trace ratio) criterion, MSFS(multi-cluster feature selection) and EVSC(eigenvalue sensitive criterion) apply the basic properties of graph Laplacian, and select the optimal feature subsets which best preserve the manifold structure defined on the graph Laplacian. In this paper, we propose a new feature selection perspective from locally linear embedding(LLE), which is another popular manifold learning method. The main difficulty of using LLE for feature selection is that its optimization involves quadratic programming and eigenvalue decomposition, both of which are continuous procedures and different from discrete feature selection. We prove that the LLE objective can be decomposed with respect to data dimensionalities in the subset selection problem, which also facilitates constructing better coordinates from data using the principal component analysis(PCA) technique. Based on these results, we propose a novel unsupervised feature selection algorithm,called locally linear selection(LLS), to select a feature subset representing the underlying data manifold. The local relationship among samples is computed from the LLE formulation, which is then used to estimate the contribution of each individual feature to the underlying manifold structure. These contributions, represented as LLS scores, are ranked and selected as the candidate solution to feature selection. We further develop a locally linear rotation-selection(LLRS) algorithm which extends LLS to identify the optimal coordinate subset from a new space. Experimental results on real-world datasets show that our method can be more effective than Laplacian eigenmap based feature selection methods.