gaussian-mixture-models

neka-nat/probreg

Python package for point cloud registration using probabilistic model (Coherent Point Drift, GMMReg, SVR, GMMTree, FilterReg, Bayesian CPD)

ldeecke/gmm-torch

Gaussian mixture models in PyTorch.

conradsnicta/armadillo-code

Armadillo: fast C++ library for linear algebra & scientific computing - https://arma.sourceforge.net

jayshah19949596/Machine-Learning-Models

Decision Trees, Random Forest, Dynamic Time Warping, Naive Bayes, KNN, Linear Regression, Logistic Regression, Mixture Of Gaussian, Neural Network, PCA, SVD, Gaussian Naive Bayes, Fitting Data to Gaussian, K-Means

cqcn1991/Wind-Speed-Analysis

An elegant probability model for the joint distribution of wind speed and direction.

omerbsezer/Generative_Models_Tutorial_with_Demo

Generative Models Tutorial with Demo: Bayesian Classifier Sampling, Variational Auto Encoder (VAE), Generative Adversial Networks (GANs), Popular GANs Architectures, Auto-Regressive Models, Important Generative Model Papers, Courses, etc..

gionanide/Speech_Signal_Processing_and_Classification

Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].

SuperKogito/Voice-based-gender-recognition

:sound: :boy: :girl:Voice based gender recognition using Mel-frequency cepstrum coefficients (MFCC) and Gaussian mixture models (GMM)

AlexanderFabisch/gmr

Gaussian Mixture Regression

wentaoyuan/deepgmr

PyTorch implementation of DeepGMR: Learning Latent Gaussian Mixture Models for Registration (ECCV 2020 spotlight)

Mayurji/MLWithPytorch

Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch

borchero/pycave

Traditional Machine Learning Models for Large-Scale Datasets in PyTorch.

sandipanpaul21/Clustering-in-Python

Clustering methods in Machine Learning includes both theory and python code of each algorithm. Algorithms include K Mean, K Mode, Hierarchical, DB Scan and Gaussian Mixture Model GMM. Interview questions on clustering are also added in the end.

Ransaka/GMM-from-scratch

The only guide you need to learn everything about GMM

jonasrothfuss/fishervector

Improved Fisher Vector Implementation- extracts Fisher Vector features from your data

jobovy/extreme-deconvolution

Density estimation using Gaussian mixtures in the presence of noisy, heterogeneous and incomplete data

mr-easy/GMM-EM-Python

Python implementation of EM algorithm for GMM. And visualization for 2D case.

Xiaoyang-Rebecca/PatternRecognition_Matlab

Feature reduction projections and classifier models are learned by training dataset and applied to classify testing dataset. A few approaches of feature reduction have been compared in this paper: principle component analysis (PCA), linear discriminant analysis (LDA) and their kernel methods (KPCA,KLDA). Correspondingly, a few approaches of classification algorithm are implemented: Support Vector Machine (SVM), Gaussian Quadratic Maximum Likelihood and K-nearest neighbors (KNN) and Gaussian Mixture Model(GMM).

applied-geodesy/jag3d

Java·Applied·Geodesy·3D - Least-Squares Adjustment Software for Geodetic Sciences

cgre-aachen/bayseg

An unsupervised machine learning algorithm for the segmentation of spatial data sets.

pedropro/OMG_Depth_Fusion

Probabilistic depth fusion based on Optimal Mixture of Gaussians for depth cameras

bertini36/GMM

Variational Inference in Gaussian Mixture Model

jonghough/jlearn

Machine Learning Library, written in J

Wei2624/AI_Learning_Hub

AI Learning Hub for Machine Learning, Deep Learning, Computer Vision and Statistics

junlulocky/PyBGMM

Bayesian inference for Gaussian mixture model with some novel algorithms

SuperKogito/Voice-based-speaker-identification

:sound: :boy: :girl: :woman: :man: Speaker identification using voice MFCCs and GMM

Shikhargupta/Machine-Learning-and-Pattern-Recognition

Implementation of Machine Learning Algorithms

zhaoyichanghong/machine_learing_algo_python

implement the machine learning algorithms by python for studying

aakhundov/tf-example-models

TensorFlow-based implementation of (Gaussian) Mixture Model and some other examples.

starkblaze01/Artificial-Intelligence-Codes

Collection of Artificial Intelligence Algorithms implemented on various problems

siavashk/GMM-FEM

Biomechanically Constrained Point Cloud Registration Using Gaussian Mixture Models

leandrofgr/GaussianMixMCMC_Metropolis

Codes related to the publication Gaussian mixture Markov chain Monte Carlo method for linear seismic inversion

alexandra-chron/hierarchical-domain-adaptation

Code of NAACL 2022 "Efficient Hierarchical Domain Adaptation for Pretrained Language Models" paper.

dmetivie/ExpectationMaximization.jl

A simple but generic implementation of Expectation Maximization algorithms to fit mixture models.

yashv28/Color-Segmentation-using-GMM