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Description: 用于gmms的训练程序
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Size: 19647 |
Author: liuevan48 |
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Description: 高斯滤波器 matlab toolbox
For GMMs and Gaussian kernels-Gaussian filter matlab toolboxFor GMMs and Gaussian kernels
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Size: 14336 |
Author: onlyjoker |
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Description: 3 books about Gaussian Mixture Models
Detail:
CLustering with GMMs
Distance between GMMs
Initialize G-3 books about Gaussian Mixture Models
Detail:
CLustering with GMMs
Distance between GMMs
Initialize GMM
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Size: 786432 |
Author: ChipChipKnight |
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Description: OPENCV下基于高斯混合模型的图像分割，程序中还有
基于大津法的图像分割和金子塔分割。-OPENCV Based on Gaussian mixture model of image segmentation, the program also includes Otsu method based on image segmentation and the segmentation pyramid.
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Size: 497664 |
Author: jiaojiao003 |
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Description: 这篇文章是关于如何改进混合高斯法的一个综述，混合高斯法用于目标检测，目标分割。-This article is an overview of how to improve the GMMS ,the GMMS is used for target detection, object segmentation.
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Size: 1306624 |
Author: 黄鹏 |
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Description: 基于混合高斯模型的目标检测，静止摄像机下的视频序列-Gaussian mixture model-based object detection, static camera video sequence under
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Size: 21504 |
Author: lei |
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Description: This introduction to the expectation–maximization (EM) algorithm
provides an intuitive and mathematically rigorous understanding of
EM. Two of the most popular applications of EM are described in
detail: estimating Gaussian mixture models (GMMs), and estimat-
ing hidden Markov models (HMMs). EM solutions are also derived
for learning an optimal mixture of fi xed models, for estimating the
parameters of a compound Dirichlet distribution, and for dis-entangling
superimposed signals. Practical issues that arise in the use of EM are
discussed, as well as variants of the algorithm that help deal with these
challenges.,This introduction to the expectation–maximization (EM) algorithm
provides an intuitive and mathematically rigorous understanding of
EM. Two of the most popular applications of EM are described in
detail: estimating Gaussian mixture models (GMMs), and estimat-
ing hidden Markov models (HMMs). EM solutions are also derived
for learning an optimal mixture of fi xed models, for estimating the
parameters of a compound Dirichlet distribution, and for dis-entangling
superimposed signals. Practical issues that arise in the use of EM are
discussed, as well as variants of the algorithm that help deal with these
challenges.
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Size: 892928 |
Author: steve |
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Description: his package contains a set of functions for calling interfacing with
HTK from Matlab. Right now its mostly limited to training GMMs and
HMMs. It converts your Matlab data into a format that HTK understands
and calls HTK command line programs. The path to the HTK binaries is
hardcoded in get_htk_path.m-his package contains a set of functions for calling interfacing with
HTK from Matlab. Right now its mostly limited to training GMMs and
HMMs. It converts your Matlab data into a format that HTK understands
and calls HTK command line programs. The path to the HTK binaries is
hardcoded in get_htk_path.m
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Size: 2367488 |
Author: abdo |
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Description: Demonstration of the reproduction of a generalized trajectory through Gaussian Mixture Regression (GMR), when considering two independent constraints represented separately in two Gaussian Mixture Models (GMMs). Through regression, a smooth generalized trajectory satisfying the constraints encapsulated in both GMMs is extracted, with associated constraints represented as covariance matrices.
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Size: 18432 |
Author: student vala |
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Description: 语音处理GMM相关算法,1.计算概率密度并画出高斯混合模型，2.计算边际，条件混合高斯密度,3估计两个GMM模型的Kullback-Leibler divergence。-GMM relating to speech processing algorithms.1,to calculate probability densities from or plot a Gaussian mixture model.2,marginal and conditional Gaussian mixture densities. 3, Kullback-Leibler divergence between two GMMs .
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Size: 21504 |
Author: 王愈 |
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Description: In this paper, we figure out the use of appended jitter and shimmer speech features for closed set text
independent speaker identification system. Jitter and shimmer features are extracted from the
fundamental frequency contour and added to baseline spectral features, specifically Mel-frequency
Cepstral Coefficients (MFCCs) for human speech and MFCC-GC which integrate the Gammachirp
filterbank instead of the Mel scale. Hidden Markov Models (HMMs) with Gaussian Mixture Models
(GMMs) state distributions are used for classification. Our approach achieves substantial performance
improvement in a speaker identification task compared with a state-of-the-art robust front-end in a
clean condition.
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Size: 256000 |
Author: mansouri |
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Description: 基于高斯混合模型的图像分割程序，结合OpenCV,包括OTSU、金字塔分割、自适应阈值分割-Image segmentation program based on Gaussian mixture model, combined with OpenCV, including OTSU, pyramid segmentation, adaptive thresholding
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Size: 2054144 |
Author: user |
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Description: matlab中的高斯混合模型代码，机器学习基础学习-Gaussian mixture model matlab code, machine learning based learning
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Size: 2048 |
Author: 振超 张 |
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Description: function对数据EM算法进行fit，并对产生的高斯混合模型的最大似然估计进行绘图。输出结构体obj，带有高斯混合模型的参数mu，sigma。(Function carries out fit for data EM algorithm, and draws the maximum likelihood estimation of the Gauss mixture model. The output structure is obj, with the parameter mu and sigma of Gauss mixture model.)
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Size: 1024 |
Author: RaymondW |
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Description: 机器学习作业GMMs算法实现，机器学习课程的一个小作业，没什么用。(Implementation of machine learning task GMMs algorithm)
Platform: |
Size: 2048 |
Author: js111 |
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