Study on the method and system implementation of an isolated word recognition system used in noisy environment

I.IntroductionRecentlytherearemanykindsofsystemsandproductsforspeechrecognition,butalmostallofthemareworkinginquietenvironment,theperformancearedegradedorevencan'tworkwhenitisoperatedinhighnoisyenvironmentssuchasincockpits,vehicle,workshopsetc.SonoiserobustnesshasbecomeoneofthemainobstaclesfortherealaPplicationsoftheautomaticspeechrecognizersanditattractstheattentionofresearchersinspeechtechnologyareas.Since1978,substantialeffortshavebeendevotedtotestandevaluatethespeechrecognizersusedinfight…     

语音情感识别中的特征选择方法

为了解决传统卷积神经网络在识别中文语音时预测错误率较高、泛化性能弱的问题,首先以深度卷积神经网络(DCNN)-连接时序分类(CTC)为研究对象,深入分析了不同卷积层、池化层以及全连接层的组合对其性能的影响;其次,在上述模型的基础上,提出了多路卷积神经网络(MCNN)-连接时序分类(CTC),并联合SENet提出了深度SE-MCNN-CTC声学模型,该模型融合了MCNN与SENet的优势,既能加强卷积神经网络的深层信息的传递、避免梯度问题,又可以对提取的特征图进行自适应重标定。最终实验结果表明:SE-MCNN-CTC相较于DCNN-CTC错误率相对降低13.51%,模型最终的错误率达22.21%;算法改进后的声学模型可以有效地提升泛化性能。     

基于STA-CRNN模型的语声情感识别*

语声情感识别对人机交互和情感计算研究领域具有重要作用,各类研究方法层出不穷。近期研究学者应用卷积神经网络和长短期记忆网络方法提取对数Mel谱图空间特征和时间特征,取得了一定的成果。然而不论是卷积神经网络还是长短期记忆网络提取特征时,都会产生特征冗余,导致语声情感识别效果下降。针对这一问题,该文提出了一种基于时空注意力机制的卷积-递归神经网络模型,采用对数Mel谱图和其一阶差分、二阶差分作为特征输入,在使用卷积神经网络提取空间特征和长短期记忆网络提取时间特征时,加入空间注意力和时间注意力机制,从而使上述网络能够更好地提取到对数Mel谱图中有效表征情感的空间特征和时间特征。该模型在Emo-DB和IEMOCAP语声数据集上的加权准确率分别达到86.8%、69.4%,未加权准确率分别达到84.7%、65.5%,优于当前大多数先进方法。     

多重分形在掌纹识别中的研究

通过对掌纹的概率密度分布和配分函数的分析，得到掌纹分布具有一定的多重分形性.进一步求取掌纹多重分形谱的宽度、极大值以及谱曲线的不对称程度，并提出以这些参数作为掌纹识别的特征量.这可能为多重分形理论在生物特征识别领域中的应用带来新的思路与方法. 关键词： 分形 模式识别     

低照度条件下超分辨率人脸图像采集系统设计与实现

在计算机技术高速发展的时代,多平台计算机视觉库随之产生。OpenCV作为一种开源代码的计算机视觉库,以可兼容多平台、接口广泛的特点被广泛运用各个领域。在低照度条件下,会出现光照环境差异过大或光线不足等情况,导致传统图像采集系统不能采集高质量的人脸图像,局限性较差。提出基于OpenCV在C 环境配置下运用三维人脸识别技术算法,设计一套低照度条件下超分辨率人脸图像采集系统。实验证明,该设计方案具有实时(对焦速度快)、快速(单张采集0.05秒)、准确(面部识别率99.3%)等特点,能够充分满足低照度条件下超分辨率人脸图像采集的需求。     

基于DSP的静脉识别系统

提出了一种基于ADSP-BF533的静脉识别系统设计方法。整个系统由静脉图像采集、静脉图像处理和识别两个模块组成。静脉图像由普通黑白模拟摄像机在近红外光照下获取;静脉图像处理和识别的核心单元是一颗ADI生产的Blackfin 533 DSP(ADSP-BF533),其速度可以满足后端处理的需要,可以实现静脉图像细化、特征提取及识别等功能。     

A palmprint recognition method based on multi-step representation

Sparse representation uses all training samples to represent a test sample only once, which can be regarded as a one step representation. However, in palmprint recognition, the appearances of palms are highly correlated which means the information provided by all the training samples are redundant while using the representation-based methods. Hence, how to obtain suitable samples for representation deserves exploring. In this paper, we devise a multi-step representation manner to extract the most representative samples for representation and recognition. In addition, the proposed sample selection strategy is based on contributions of the classes, not merely the effort of a single sample. Compared with some other appearance-based methods, the proposed method obtained a competitive result on PolyU multispectral palmprint database.     

基于ARM的嵌入式图像识别检测系统的设计

图像识别的主要目的是使用计算机作为工具对目标图像进行处理、解析与应用,通过数据分析检测出具有不同特征的目标和对象,发展至今其已成为了人工智能的基础。本文基于ARM嵌入式芯片提出了一种结合尺度不变特征变换匹配算法的图像识别检测系统,系统硬件部分采用模块化设计的思想以提高系统的兼容性,分为图像获取、数据采集、数据存储、图像识别等模块;软件部分采用斑点检测匹配匹配算法进行图像识别以提高图像识别速度与精度。实验结果表明所设计的系统具有识别速度快、精度高、可靠性高、故障少的特点。     

A two-phase face recognition method in frequency domain

In this paper, we propose a two-phase face recognition method in frequency domain using discrete cosine transform (DCT) and discrete Fourier transform (DFT). The absolute values of DCT coefficients or DFT amplitude spectra are used to represent the face image, i.e. the transformed image. Then a two-phase face classification method is applied to the transformed images. This method is as follows: its first phase uses the Euclidean distance formula to calculate the distance between a test sample and each sample in the training sets, and then exploits the Euclidean distance of each training sample to determine K nearest neighbors for the test sample. Its second phase represents the test sample as a linear combination of the determined K nearest neighbors and uses the representation result to perform classification. In addition, we use various numbers of DCT coefficients and DFT amplitude spectra to test the effect on our algorithms. The experimental results show that our method outperforms the two-phase face recognition method based on space domain of face images.     

A temporal hand gesture recognition system based on hog and motion trajectory

A real-time, rapid and robust gesture recognition system is usually hindered by difficulty of hand localization and complexity of hand gesture modeling, especially under complex background. For eliminating these obstacles, in this paper, we propose a method using histograms of oriented gradients features (HOG) and motion trajectory information for temporal hand gesture recognition in natural environment. We firstly localize hand in video stream based on hand detection by HOG and support vector machine algorithm (SVM). After hand localization, the motion trajectory information of consecutive hand gesture is extracted and a database of standard gestures is built. Finally, the Mahalanobis distance between input gesture and database is computed for recognition. As the experimental results shown, our method exhibits a good performance in real-time test.     

双多通道虹膜识别系统的研究

在双多通道虹膜识别系统中,研究了局部区域通道对识别率的影响,提出了双向式区域划分和通道数的优化。研究了小波滤波器通道对识别能力的影响和滤波器通道的选择,给出了引入小波包通道来提升识别率的新方法。通过对WED(Weighted Euclidean Distance)分类器的改进研究,提出了相对WED分类器的新概念,进一步提高了系统的识别能力。     

Real-time blind source separation system with applications to distant speech recognition

A real-time BSS system based on DUET was developed and implemented in order to assess its potential as the front-end for a DSR engine. The system uses only two closely-spaced standard omni-directional microphones and a computer soundcard and was developed for low reverberation environments with several human speakers and different noise sources.     

一种用于光纤链路振动信号模式识别的规整化复合特征提取方法

相位光时域反射链路监测系统是一种利用光纤作为传感介质的传感系统, 能够监测、定位、识别入侵信号.模式识别模块是其重要组成部分, 实时智能区分安全扰动和危险入侵.本文提出一种用于光纤链路振动信号模式识别的复合特征提取方法.利用改进的双门限方法确定有效信号段的起止位置, 结合最大能量与最高信噪比挑选出采样周期内主要入侵扰动的特征段.综合利用特征段时域持续时间和小波包能量谱提取复合特征向量, 使用支持向量机进行模式识别.实验表明, 基于本文提出的规整化特征提取方法的模式识别准确率有了显著提高.     

A multi-target approach for isolated word recognition using artificial neural network

I.TntroductionStatisticalandneuralnetworkmcthodsforpatternclassiflcationusesignificantlydifTerentapproachesintrainingaclassificr.Inthestatistica1approach,thcformationofaclassifier1arge1ydependsonthestatisticsofthetrainingpatternsand,insomecases,theassumptionsaboutthedistributionofthepopulation.Theneuralnetworkmethodisnon-parametricandcanbeadaptivcinthetrainingprocessl'l.Becauseofitssimplicityandflexibility,theneuralnetworkhasbecnincreasing1yusedforpatternclassiflcation.Itisnowwe11knownthatan…     

基于OPENCV的手势识别系统的设计与实现

人与计算机的交互技术是一种新型的计算机技术,且逐渐演变为一种主流技术和计算机领域的技术热点。为了能够更好的识别手势和跟踪手势的运动轨迹,提出了基于OPENCV的手势识别系统,系统引入了OPENCV计算机视觉库,OPENCV作为优秀的计算机视觉库,为设计的实现提供了便捷的代码,利用OPENCV技术中的图像处理算法,首现通过摄像头采集数据图像,并对采集到的图像进行一系列的缩放,去噪以及锐化等处理,然后对人体手势建立肤色模型,然后经过灰度阈值化来转换成二值图像,得到手轮廓的数据图像后,采用轮廓匹配方法识别出手型。最后通过10种基本的手势模型对比验证了本系统具有一定的实时性,并且识别率可以达到95%以上。     

光纤传感颜色识别系统的设计与实现

利用光电传感技术进行颜色识别可以克服传统识别方法的许多不足,能辨别出微小的颜色变化并大幅度提高测试精确度。通过探测物体的后向反射光,一种采用反射式数字光纤传感器设计的颜色识别系统得以实现。由于不同颜色的被测物体对光信号吸收和反射能力不同,它可以根据反射光的强弱来识别颜色。实验证明采用光纤传感器可以实现近距离范围内对被测物体任意区域的非接触式测量,测量范围宽、测试精度高,在表面探测中具有很好的应用前景。     

A sparse representation method based on kernel and virtual samples for face recognition

To improve the classification accuracy of face recognition, a sparse representation method based on kernel and virtual samples is proposed in this paper. The proposed method has the following basic idea: first, it extends the training samples by copying the left side of the original training samples to the right side to form virtual training samples. Then the virtual training samples and the original training samples make up a new training set and we use a kernel-induced distance to determine M nearest neighbors of the test sample from the new training set. Second, it expresses the test sample as a linear combination of the selected M nearest training samples and finally exploits the determined linear combination to perform classification of the test sample. A large number of face recognition experiments on different face databases illustrate that the error ratios obtained by our method are always lower more or less than face recognition methods including the method mentioned in Xu and Zhu [21], the method proposed in Xu and Zhu [39], sparse representation method based on virtual samples (SRMVS), collaborative representation based classification with regularized least square (CRC_RLS), two-phase test sample sparse representation (TPTSSR), and the feature space-based representation method.     

A face recognition algorithm based on collaborative representation

In this paper, we propose a face recognition algorithm by incorporating a neighbor matrix into the objective function of sparse coding. We first calculate the neighbor matrix between the test sample and each training sample by using the revised reconstruction error of each class. Specifically, the revised reconstruction error (RRE) of each class is the division of the l₂-norm of reconstruction error to the l₂-norm of reconstruction coefficients, which can be used to increase the discrimination information for classification. Then we use the neighbor matrix and all the training samples to linearly represent the test sample. Thus, our algorithm can preserve locality and similarity information of sparse coding. The experimental results show that our algorithm achieves better performance than four previous algorithms on three face databases.     

植物油颜色的谱识别方法

本文提出一种植物油色的谱识别新方法.该方法突破了传统的三刺激值测色模式,提出了用光谱特征及非固定的场景模式,替代三刺激值及其固定的CIE测量模式,建立了良好的判别函数,适于植物油色的客观检验与定级.结果令人满意.