A classification method for crowded situation using environmental sounds based on Gaussian mixture model-universal background model

2016 ◽  
Vol 140 (4) ◽  
pp. 3110-3110
Author(s):  
Tomoyasu Tanaka ◽  
Sunao Hara ◽  
Masanobu Abe
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Background Model ◽  
Classification Method ◽  
Environmental Sounds ◽  
Universal Background Model

scholarly journals GMM-UBM Based Modeling for Language Identification using New Feature Vectors

2020 ◽  
Vol 9 (4) ◽  
pp. 3034-3039
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Language Identification ◽  
Training Data ◽  
Background Model ◽  
Indian Languages ◽  
Feature Vectors ◽  
Universal Background Model ◽  
New Feature

The most of the existing LID systems based on the Gaussian Mixture model. The main requirement of the GMM based LID system is it require large amount of speech data to train the GMM model. Most of the Indian languages have the similarity because they are derived from Devanagari. Even though common phonemes exists in phoneme sets across the Indian languages, each language contain its unique phonotactic constraints imposed by the language. Any modeling technique capable of capturing all these slight variations imposed by the language is one of the important language identification cue. To model the GMM based LID system which captures above variations it require large number of mixture components.To model the large number of mixture components using Gaussian Mixture Model (GMM), the technique requires a large number of training data for each language class, which is very difficult to get for Indian languages. The main objective of GMM-UBM based LID system is it require less amount of training data to train(model) the system. In this paper, the importance of GMM-UBM modeling for language identification (LID) task for Indian languages are explored using new set of feature vectors. In GMM-UBM LID system based on the new feature vectors, the phonotactic variations imparted by different Indian languages are modeled using Gaussian Mixture model and Universal Background Model (GMM-UBM) technique. In this type of modeling, some amount of data from each class of language is pooled to create a universal background model. From this UBM model each model class is adapted. In this study, it is found that the performance of new feature vectors GMM-UBM based LID system is superior when compared to conventional new feature vectors based GMM LID system.

scholarly journals A Review on Text-Independent Speaker Verification Techniques in Realistic World

2016 ◽  
Vol 9 (1) ◽  
pp. 36-40
Author(s):  
Renu Singh ◽  
Arvind Singh ◽  
Utpal Bhattacharjee
Keyword(s):  
Support Vector Machine ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Speaker Verification ◽  
Gaussian Mixture ◽  
Background Model ◽  
Support Vector ◽  
Universal Background Model ◽  
Verification Techniques ◽  
Text Independent Speaker Verification

This paper presents a reviewof various speaker verification approaches in realistic world, and explore a combinational approach between Gaussian Mixture Model (GMM) and Support Vector Machine (SVM) as well as Gaussian Mixture Model (GMM) and Universal Background Model (UBM).

Algorithm of Moving Vehicle Detection by Improved Gaussian Mixture Model Based on ROI

2015 ◽  
Vol 734 ◽  
pp. 463-467 ◽  
Author(s):  
Pan Pan Zhang ◽  
Chun Yang Mu ◽  
Xing Ma ◽  
Fu Lu Xu
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Region Of Interest ◽  
Gaussian Mixture ◽  
Background Model ◽  
Morphological Filter ◽  
Improved Method ◽  
Moving Vehicle ◽  
Model Based ◽  
Moving Vehicle Detection

Detection of moving object is a hot topic in computer vision. Traditionally, it is detected for every pixel in whole image by Gaussian mixture background model, which may waste more time and space. In order to improving the computational efficiency, an advanced Gaussian mixture model based on Region of Interest was proposed. Firstly, the solution finds out the most probably region where the target may turn up. And then Gaussian mixture background model is built in this area. Finally, morphological filter algorithm is used for improving integrity of the detected targets. Results show that the improved method could have a more perfect detection but no more time increasing than typical method.

scholarly journals Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification

2020 ◽  
Vol 66 (4) ◽  
pp. 215-226 ◽  
Author(s):  
Branislav Panić ◽  
Jernej Klemenc ◽  
Marko Nagode
Keyword(s):  
Fault Detection ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Condition Monitoring ◽  
Gaussian Mixture ◽  
Classification Method ◽  
Fault Classification ◽  
Classification Methods ◽  
Bearing Fault ◽  
Model Based

Condition monitoring and fault detection are nowadays popular topic. Different loads, enviroments etc. affect the components and systems differently and can induce the fault and faulty behaviour. Most of the approaches for the fault detection rely on the use of the good classification method. Gaussian mixture model based classification are stable and versatile methods which can be applied to a wide range of classification tasks. The main task is the estimation of the parameters in the Gaussian mixture model. Those can be estimated with various techniques. Therefore, the Gaussian mixture model based classification have different variants which can vary in performance. To test the performance of the Gaussian mixture model based classification variants and general usefulness of the Gaussian mixture model based classification for the fault detection, we have opted to use the bearing fault classification problem. Additionally, comparisons with other widely used non-parametric classification methods are made, such as support vector machines and neural networks. The performance of each classification method is evaluated by multiple repeated k-fold cross validation. From the results obtained, Gaussian mixture model based classification methods are shown to be competitive and efficient methods and usable in the field of fault detection and condition monitoring.

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