Results 191 to 200 of about 33,807 (240)

Bearing Fault Diagnosis Using Gaussian Mixture Models (GMMs)

Applied Mechanics and Materials, 2007
This paper presents a novel method for bearing fault diagnosis based on wavelet transform and Gaussian mixture models (GMMs). Vibration signals for normal bearings, bearings with inner race faults, outer race faults and ball faults were acquired from a motor-driven experimental system. The wavelet transform was used to process the vibration signals and
J. Sun, Gang Yu, Chang Ning Li
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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.
null Gang Yu, Jun Sun, null Changning Li
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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. Yucel Ozbek, Mubeccel Demirekler
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Speaker Verification Using Gaussian Mixture Model (GMM)

2011
This paper applies GMM for SV on Malay speech. The speaker models were trained on maximum likelihood estimated. The system was evaluated with 23 client speakers with 56 imposters. Malay clean speech data was used. 20 training of 3.5s utterances are used. The best performance achieved using 256-Gaussian imposter model and 32-Gaussian client model gave 3.
Shaikh Salleh, Sheikh Hussain, Ting, C. M., Ariff, A. K., Kamarulafizam, I., Suraya, R. A., Hussain, H.   +5 more
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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
null Xiaodan Zhuang, Jing Huang, Gerasimos Potamianos, Mark Hasegawa-Johnson   +3 more
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Gaussian Mixture Model (GMM) Based Dynamic Object Detection and Tracking

2019 International Conference on Unmanned Aircraft Systems (ICUAS), 2019
In this paper, we have addressed the problem of real-time detection and tracking of dynamic objects using quadrotors. We have developed a novel object detection algorithm by analyzing and matching the color and spacial features of the target from monocular image sequences.
Vishnu Anand, Durgakant Pushp, Rishin Raj, Kaushik Das   +3 more
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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
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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
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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
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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
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