Results 161 to 170 of about 7,207 (210)

Audiovisual articulatory inversion based on Gaussian Mixture Model (GMM)

2010 IEEE 18th Signal Processing and Communications Applications Conference, 2010
In this study, we examined articulatory inversion using audiovisual information based on Gaussian Mixture Model (GMM). In this method the joint distribution of the articulatory movement and audio (and/or visual) data are modelled via a mixture of Gaussians.
I Yücel Özbek, Mubeccel Demirekler
exaly   +2 more sources

Machine performance assessment using Gaussian mixture model (GMM)

2008 2nd International Symposium on Systems and Control in Aerospace and Astronautics, 2008
In this paper, we present a simple and efficient machine performance assessment approach based on Gaussian mixture model (GMM). By only utilizing the machine performance signatures generated from normal machine operation, a GMM can be trained to model the underlying density distribution of the training data.
Changning Li
exaly   +2 more sources

Speaker Identification Performance Enhancement using Gaussian Mixture Model with GMM Classification Post-Processor

2007 IEEE International Conference on Signal Processing and Communications, 2007
In this paper the application of Gaussian mixture model (GMM) classifier is investigated as an efficient post-processing method to enhance the performance of GMM-based speaker identification systems; such as Gaussian mixture model universal background model (GMM-UBM) scheme.
Hamid Reza Sadegh Mohammadi, Rahim Saeidi   +1 more
exaly   +2 more sources

Muscular activation intervals detection using gaussian mixture model GMM applied to sEMG signals

2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom), 2016
We propose to apply the Gaussian Mixture Model (GMM) to surface electromyography (sEMG) signals in order to detect the muscular activation (MA) onset, timing off and intervals. First, classical time and frequency features are extracted from the sEMG signals, beside the Teager-Kaiser energy operator (TKEO) is evaluated and added as a new feature which ...
Philippe Ravier, Olivier Buttelli
exaly   +2 more sources

PRIMAL-GMM: PaRametrIc MAnifold Learning of Gaussian Mixture Models

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022
We propose a ParametRIc MAnifold Learning (PRIMAL) algorithm for Gaussian mixtures models (GMM), assuming that GMMs lie on or near to a manifold of probability distributions that is generated from a low-dimensional hierarchical latent space through parametric mappings. Inspired by principal component analysis (PCA), the generative processes for priors,
Ziquan Liu, Lei Yu 0013, Janet H. Hsiao, Antoni B. Chan   +3 more
openaire   +3 more sources

Vehicle Detection Using Machine Learning Model with the Gaussian Mixture Model (GMM)

Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 2022
Motion tracking apps are used for a lot of different things, like finding traffic jams and counting the number of cars going through a traffic light. The datasets come from many places on the internet, like YouTube and public dataset archives. There are about 20 videos that are tagged with the words "traffic" and "traffic camera video" and run for 10 ...
Rika Rosnelly, Bob Subhan Riza, Linda Wahyuni, S. Edy Victor Haryanto, Annas Prasetio   +4 more
openaire   +1 more source

Acoustic fall detection using Gaussian mixture models and GMM supervectors

2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009
We present a system that detects human falls in the home environment, distinguishing them from competing noise, by using only the audio signal from a single far-field microphone. The proposed system models each fall or noise segment by means of a Gaussian mixture model (GMM) supervector, whose Euclidean distance measures the pairwise difference between
Xiaodan Zhuang, Jing Huang 0019, Gerasimos Potamianos, Mark Hasegawa-Johnson   +3 more
openaire   +1 more source

Engine Overheating Prediction with Machine Learning Using Gaussian Mixture Model (GMM)

SAE Technical Paper Series, 2022
<div class="section abstract"><div class="htmlview paragraph">The Advancement in Connected vehicles Technology in recent years has propelled the use of concepts like the Internet of Things (IoT) and big data in the automotive industry. The progressive electrification of the powertrain has led to the integration of various sensors in the ...
Shrikant Deokrishna Hiwase, PRAMOD JAGTAP, Dinesh Krishna   +2 more
openaire   +1 more source

Driver face tracking using Gaussian mixture model(GMM)

IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683), 2004
For the purpose of driver fatigue or attention level surveillance based on various facial cues, a practical computer vision system would probably implement at least three major components: a face detection module to locate the face as system initialization, a face tracking module to track the face in the subsequent images, and an inference module to ...
Y. Zhu, K. Fujimura
openaire   +1 more source