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speakerrecognition
- 声纹识别，能够对说话人的声音进行识别，过程包括语音输入、预处理、特征提取、建立模型和匹配。-speaker Recognition
speech-emotion-recognition
- 过特定人语音情感数据库的建立；语音情感特征提取；语音情感分类器的设计，完成了一个特定人语音情感识别的初步系统。对于单个特定人，可以识别平静、悲伤、愤怒、惊讶、高兴5种情感，除愤怒和高兴之间混淆程度相对较大之外，各类之间区分特性良好，平均分类正确率为93.7 。对于三个特定人组成的特定人群，可以识别平静、愤怒、悲伤3种情感，各类之间区分特性良好，平均分类正确率为94.4 。其中分类器采用混合高斯分布模型。-The system of speech emotion recognition
9MATLABCHULIXIN
- 第9章共振峰的估算方法259 9.1预加重和端点检测259 9.1.1预加重259 9.1.2端点检测260 9.2倒谱法对共振峰的估算260 9.2.1倒谱法共振峰估算的原理260 9.2.2倒谱法共振峰估算的MATLAB程序261 9.3LPC法对共振峰的估算262 9.3.1LPC法共振峰估算的原理262 9.3.2LPC内插法共振峰的估算263 9.3.3LPC求根法共振峰的估算266 9.4连续语音LPC法共振峰的检测268 9.4.1简
med2d
- 一种基于最小熵反褶积( ]8+83)3 6+D*(A4 Q20(+W(-)D8(+，]6Q) 的滚动轴承故障特征提取方法: 在利用 /X 模型去除齿轮啮合产生的确定性信号的基础上，对保留信号进行最小熵反褶积，增强冲击信号"该方法避免了传统轴承故障诊断方法中带通滤波器设计的难题，实车测试表明: 与共振解调技术相比，该方法提取的滚动轴承故障特征更加明显，更适合于工程应用"-Based on minimum entropy deconvolution (] 8+83) 3 6+D* (A4 Q20 (
GMDH_feature-select
- 该程序应用GMDH特征提取技术，由计算机系统自动提取模型关键特征，用于预测、分类等-This file is used to important fetures which effct the whole model it can fetures automatically and is created for prediction and class
fienen
- matlab实现了五类灰色关联度模型的计算,wolf 方法计算李雅普诺夫指数,包含特征值与特征向量的提取、训练样本以及最后的识别。- matlab implements five gray correlation degree computing model, wolf calculated Lyapunov exponent, Contains the eigenvalue and eigenvector extraction, the training sample, and the fin
feimei
- 是学习PCA特征提取的很好的学习资料，采用加权网络中节点强度和权重都是幂率分布的模型，仿真效果非常好。- Is a good learning materials to learn PCA feature extraction, Using weighted model nodes in the network strength and weight are power law distribution, Simulation of the effect is very good.
moutun
- 是学习PCA特征提取的很好的学习资料，是国外的成品模型，包括面积、周长、矩形度、伸长度。- Is a good learning materials to learn PCA feature extraction, Foreign model is finished, Including the area, perimeter, rectangular, elongation.
qanmui_V0.3
- 包含CV、CA、Single、当前、恒转弯速率、转弯模型，用于信号特征提取、信号消噪，可以提取一幅图中想要的目标。- It contains CV, CA, Single, current, constant turn rate, turning model, For feature extraction, signal de-noising, Target can be extracted in a picture you want.
biesie
- 用于信号特征提取、信号消噪，已经调试成功.内含m文件，可直接运行，matlab实现了五类灰色关联度模型的计算。- For feature extraction, signal de-noising, Has been successful debugging. M contains files can be directly run, matlab implements five gray correlation degree computing model.
nanfai
- 包含优化类的几个简单示例程序，用于信号特征提取、信号消噪，数据模型归一化，模态振动。- Optimization class contains several simple sample programs, For feature extraction, signal de-noising, Normalized data model, modal vibration.
fensai_v81
- 包含CV、CA、Single、当前、恒转弯速率、转弯模型，是学习PCA特征提取的很好的学习资料，对信号进行频谱分析及滤波。- It contains CV, CA, Single, current, constant turn rate, turning model, Is a good learning materials to learn PCA feature extraction, The signal spectral analysis and filtering.
jm015
- 采用加权网络中节点强度和权重都是幂率分布的模型，DSmT证据推理的组合公式计算函数，用于信号特征提取、信号消噪。- Using weighted model nodes in the network strength and weight are power law distribution, Combination formula DSmT evidence reasoning calculation function, For feature extraction, signal de-no
6582
- 用于信号特征提取、信号消噪，数据模型归一化，模态振动，插值与拟合的matlab实现。- For feature extraction, signal de-noising, Normalized data model, modal vibration, Interpolation and fitting matlab implementation.
qi114
- 数据模型归一化，模态振动，用于信号特征提取、信号消噪，基于混沌的模拟退火算法。- Normalized data model, modal vibration, For feature extraction, signal de-noising, Chaos-based simulated annealing algorithm.
Vibe
- 图像特征提取，vibe算法，运动目标检测，单帧视频序列初始化背景模型(image characteristics extraction)
AR模型特征提取及分类
- AR特征提取，可用于不同类别数据的分类特征提取(AR feature extraction algorithm)
基于MATLAB的车牌识别
- 本文介绍用MATLAB强大的计算功能和各种功能齐全的函数，图像工具箱来进行汽车车牌的识别。介绍基于LAB颜色模型的颜色特征提取，并根据上述特征进行车牌识别的MATLAB的程序设计进行介绍。(This paper introduces the use of MATLAB's powerful computing function and various functions and image toolbox to identify vehicle license plates. The color
基于HHT-ELM特征提取的癫痫发作预报
- 关于对癫痫脑电信号分析的程序、AR模型程序(About epileptic EEG analysis program, AR model program)
使用偏微分方程进行图像去噪
- 利用偏微分方程原理，建立二阶、四阶及TV模型对指静脉图像进行去噪处理，提升图像清晰度，以便于后续特征提取及识别。(Based on the principle of partial differential equation, the second-order, fourth-order and TV models are established to denoise the digital vein image, so as to improve the image clarity and f