搜索资源列表
Test_GMM
- gmm test... gmm test... gmm test
GMMv1051014
- gmm test gmm test
GMM 用高斯更新背景
- 用高斯跟新背景，然后将对象提取出来
GMM train
- 用于gmms的训练程序
JAG3Dv
- JAG3D is a simple tool to estimate a local geodetical 3d-net by a least-square-adjustment (LSA) called Gauß -Markov-Model (GMM). The software calculates coordinates from raw-data e.g. slopedistance, directions, azimuth-angles and/or zenith-angle
final-project
- speaker recognition system
voice-conversion--MFCC-GMM
- 实现多个人的说话人识别，基于gmm模型，用mfcc参数训练-voice conversion based on gmm
GMM
- This file has the code for GMM classification technique
GMM
- 基于GMM说话人识别特征提取 1、需要有语音工具箱 2、训练和测试包含了加噪的代码 3、包含噪声数据 4、没有包含语音数据 5、语音数据可以自行录制或者找一段语音-Extraction Based on GMM speaker recognition feature A need to have a voice toolbox 2, the training and testing included adding noise code 3, including n
GMM-latentSpace-v2.0
- GMM算法，利用EM算法求解混合模型中每个模型的参数-Gaussian Mixture Model,GMMalgorith,Use EM algorith
note-on-GMM
- 高斯混合模型GMM的学习资料，包含一个学习笔记和一个简单的C++实现-Gaussian mixture model learning materials, including notes and a simple C++ source code
Speech---Emotion-Recognition---Large-Margin-GMM--
- Speech - Emotion Recognition - Large-Margin GMM - IEEE 2012
82603023mixture_of_gaussians
- 高斯混合噪声程序, 程序完整可以直接使用很方便(this is a matlab gmm program, easy and good)
GMM
- GMM(Gaussian Mixture Model)，高斯混合模型(或者混合高斯模型)，也可以简写为MOG(Mixture of Gaussian)。(GMM(Gaussian Mixture Model))
GMM）matlab源码
- 高斯模型就是用高斯概率密度函数（正态分布曲线）精确地量化事物，将一个事物分解为若干的基于高斯概率密度函数（正态分布曲线）形成的模型。(Gaussian model is to use Gaussian probability density function (normal distribution curve) to accurately quantify things, a thing is divided into several based on the Gaussian probabi
GMM+帧差发
- 利用VC++ 关于GMM高斯+帧差法 检测移动物体(Using VC + + GMM Gaussian + frame difference method to detect moving objects)
HMM1
- 在VC6.0平台上进行编写的，包括隐马尔科夫模型（HMM）和混合高斯模型（GMM）在内的用于模板训练的算法。(The algorithm for template training is written on VC6.0 platform, including hidden Markov model (HMM) and mixed Gauss model (GMM).)
GMM_FIELD
- GMM-FIELD is a computer program that calculates the local electric field in an aggregate of spherical particles that is hit by a plane light wave.
Clustering
- 1) 使用凝聚型层次聚类算法（即最小生成树算法）对所有数据点进行聚类，最后聚成3类。相异度定义方法可选择single linkage、complete linkage、average linkage或者average group linkage中任意一种。 2) 使用C-Means算法对所有数据点进行聚类。C=3。 任务2（必做）： 使用高斯混合模型（GMM）聚类算法对所有数据点进行聚类。C=3。并请给出得到的混合模型参数（包括比例??、均值??和协方差Σ）。 任务3（全做）： 1) 参考数据文
EMM_Python2
- GMM算法说明 包括 EM、GMM、GMM参数设置、GMM机器学习(GMM code including EM,GMM. GMM_Parameter)