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

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..

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

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].

Gaussian mixture models in PyTorch.

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

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

Improved Fisher Vector Implementation

Fast clustering Expectation Maximization algorithm for Gaussian Mixture Models.

Gaussian Mixture Regression

Bayesian inference for Gaussian mixture model with some novel algorithms

Behavioral segmentation of open field in DeepLabCut, or B-SOID ("B-side"), is an unsupervised learning algorithm written in MATLAB and Python that serves to discover behaviors that are not pre-defined by users.

🔉 👦 👧Voice based gender recognition using Mel-frequency cepstrum coefficients (MFCC) and Gaussian mixture models (GMM)

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

Implementation of Machine Learning Algorithms

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

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

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

Variational Inference in Gaussian Mixture Model

Machine Learning Library, written in J

Collection of Artificial Intelligence Algorithms implemented on various problems

Biomechanically Constrained Point Cloud Registration Using Gaussian Mixture Models

implement the machine learning algorithms by python for studying

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).

Implementation of Background Substraction using Gaussian mixture model and using OpenCV library.

Involves the OpenCV based C++ implementation to detect and track roads for almost realtime performance

Implementations of machine learning algorithm by Python 3

This VAD library can process audio in real-time utilizing GMM which helps identify presence of human speech in an audio sample that contains a mixture of speech and noise.