A neural network based model for VoIP speech quality prediction

2009 ◽  
Author(s):  
Jiuchun Ren ◽  
Dilin Mao ◽  
ZhiWei Wang
Keyword(s):  
Neural Network ◽  
Speech Quality ◽  
Quality Prediction

Non-intrusive speech quality prediction in VoIP networks using a neural network approach

2009 ◽  
Vol 72 (10-12) ◽  
pp. 2595-2608 ◽  
Author(s):  
M. AL-Akhras ◽  
H. Zedan ◽  
R. John ◽  
I. ALMomani
Keyword(s):  
Neural Network ◽  
Speech Quality ◽  
Quality Prediction ◽  
Network Approach ◽  
Neural Network Approach

A Novel Recurrent Generalized Congruence Neural Network Measure for Objective Speech Quality Evaluation

2012 ◽  
Vol 433-440 ◽  
pp. 2282-2287
Author(s):  
Tian Yun Yan
Keyword(s):  
Neural Network ◽  
Quality Evaluation ◽  
Speech Quality ◽  
System Model ◽  
The Novel ◽  
Training Time ◽  
Network Measure ◽  
Comparison Results ◽  
Objective Speech Quality ◽  
New System

A new system model for objective speech quality evaluation based on the improved recurrent generalized congruence neural network (RGCNN/OSQE) is proposed. The performance of the RGCNN model is compared with the most commonly used RBFNN (radial basis function neural network) model in objective speech quality evaluation. Comparison results show that the RGCNN model has higher correlation coefficient, less deviation, and saves about half training time, i.e., the RGCNN model has obvious advantages over the RBFNN model. Therefore, the novel RGCNN model for objective speech quality evaluation is feasible and effective.

Intelligent Prediction on the Quality of Cementing

2011 ◽  
Vol 84-85 ◽  
pp. 373-377
Author(s):  
Wei Zhang Wang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Influential Factor ◽  
Software Systems ◽  
Neural Network Modeling ◽  
Quality Prediction ◽  
Modeling Tools ◽  
Fuzzy Neural ◽  
Neural Network System

The present solutions of well cementing are mostly designed by designers’ experience and calculation which can not predict the engineering quality after application of the designs. Meanwhile some questions in the designs can not be solved before construction. On the basis of detailed evaluation of every influential factor according to construction and environmental conditions, this article provides cementing fuzzy neural network model by means of 2nsoftEditor neural network modeling tools, and the stable software systems with the combination of artificial neural network and fuzzy logic rules are expected to improve the credibility of cementing quality prediction. Construction practice shows that cementing quality prediction with application of fuzzy neural network system before cementing can greatly reduce the cementing costs and improve the cementing success ratio.

scholarly journals Estimation of distortion sensitivity for visual quality prediction using a convolutional neural network

2019 ◽  
Vol 91 ◽  
pp. 54-65 ◽  
Author(s):  
Sebastian Bosse ◽  
Sören Becker ◽  
Klaus-Robert Müller ◽  
Wojciech Samek ◽  
Thomas Wiegand
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Visual Quality ◽  
Quality Prediction
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