Results 181 to 190 of about 34,052 (223)

New image reconstruction algorithm for CCERT: LBP + Gaussian mixture model (GMM) clustering

Measurement Science and Technology, 2020
Abstract This work focuses on the study of the image reconstruction algorithm of capacitively coupled electrical resistance tomography (CCERT). With the combination of a linear back projection (LBP) algorithm and an unsupervised Gaussian mixture model (GMM) algorithm, a new image reconstruction algorithm for CCERT is proposed. The LBP
Yuxin Wang, Xuekai He, Yandan Jiang, Baoliang Wang, Haifeng Ji, Zhiyao Huang   +5 more
openaire   +1 more source

Automatic Seizure Detection Using Logarithmic Euclidean-Gaussian Mixture Models (LE-GMMs) and Improved Deep Forest Learning

IEEE Journal of Biomedical and Health Informatics, 2023
Automatic seizure detection could facilitate early detection, improve treatment planning, and reduce medical workload. This study describes a novel Logarithmic Euclidean-Gaussian Mixture Models (LE-GMMs) and an improved Deep Forest learning algorithm for epileptic seizure detection.
Shasha Yuan, Xiang Liu, Junliang Shang, Jin-Xing Liu, Juan Wang, Weidong Zhou   +5 more
openaire   +2 more sources

A Session-GMM Generative Model Using Test Utterance Gaussian Mixture Modeling for Speaker Verification

Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., 2006
Test utterance parameterization (TUP) using Gaussian mixture models (GMMs) has recently been shown to be beneficial for speaker indexing due to its computational efficiency and identical accuracy compared to classic GMM-based recognizers. We show that TUP can also lead to more accurate speaker recognition.
H. Aronowitz, D. Burshtein, A. Amir
openaire   +1 more source

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 ...
Naseem, Amal, Jabloun, Meryem, Ravier, Philippe, Buttelli, Olivier   +3 more
openaire   +1 more source

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.
H. R. Sadegh Mohammadi, R. Saeidi
openaire   +1 more source

Gaussian Mixture Model (GMM) Based Object Detection and Tracking using Dynamic Patch Estimation

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019
In this paper, we have developed a Gaussian Mixture Model (GMM) based algorithm with dynamic patch estimation for real-time detection and tracking of a known object. This research work detects the object of interest, estimates its 3-D position using Extended Kalman Filter (EKF) and generates the control output to the quad-rotor to track the target. The
Vishnu Anand, Durgakant Pushp, Rishin Raj, Kaushik Das   +3 more
openaire   +1 more source

Unsupervised Learning of Finite Gaussian Mixture Models (GMMs): A Greedy Approach

2011
In this work we propose a clustering algorithm that learns on-line a finite gaussian mixture model from multivariate data based on the expectation maximization approach. The convergence of the right number of components as well as their means and covariances is achieved without requiring any careful initialization.
Nicola Greggio, Alexandre Bernardino, José Santos-Victor   +2 more
openaire   +1 more source

SPEAKER IDENTIFICATION BY AGGREGATING GAUSSIAN MIXTURE MODELS (GMMs) BASED ON UNCORRELATED MFCC-DERIVED FEATURES

International Journal of Pattern Recognition and Artificial Intelligence, 2014
For solving speaker identification problems, the approach proposed by Reynolds [IEEE Signal Process. Lett.2 (1995) 46–48], using Gaussian Mixture Models (GMMs) based on Mel Frequency Cepstral Coefficients (MFCCs) as features, is one of the most effective available in the literature.
AMITA PAL, SMARAJIT BOSE, GOPAL K. BASAK, AMITAVA MUKHOPADHYAY   +3 more
openaire   +1 more source

An Adaptive Segmentation Method Based on Gaussian Mixture Model (GMM) Clustering for DNA Microarray

2014 International Conference on Intelligent Computing Applications, 2014
Microarray allows us to efficiently analyse valuable gene expression data. In this paper we propose a effective methodology for analysis of microarrays. Earlier a new gridding algorithm is proposed to address all individual spots and to determine their borders.
M. Parthasarathy, R. Ramya, A. Vijaya
openaire   +1 more source

An Efficient GMM Classification Post-Processing Method for Structural Gaussian Mixture Model Based Speaker Verification

2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, 2006
In this paper a Gaussian mixture model (GMM) classifier, called GMM identifier, is proposed as an efficient post-processing method to enhance the performance of a CMM-based speaker verification system; such as Gaussian mixture model universal background model (GMM-UBM) and structural Gaussian mixture models with structural background model (SGMM-SBM ...
R. Saeidi, H.R. Sadegh Mohammadi, M.K. Amirhosseini   +2 more
openaire   +1 more source