Optimal prosodic feature extraction and classification in parametric excitation source information for Indian language identification using neural network based Q-learning algorithm

2018 ◽  
Vol 22 (1) ◽  
pp. 67-77 ◽  
Author(s):  
Himanish Shekhar Das ◽  
Pinki Roy
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Parametric Excitation ◽  
Learning Algorithm ◽  
Language Identification ◽  
Excitation Source ◽  
Source Information ◽  
Indian Language ◽  
Q Learning ◽  
Prosodic Feature

scholarly journals Indian Language Identification using Deep Learning

2020 ◽  
Vol 32 ◽  
pp. 01010
Author(s):  
Shubham Godbole ◽  
Vaishnavi Jadhav ◽  
Gajanan Birajdar
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Classification Accuracy ◽  
Visual Representations ◽  
Spoken Language ◽  
Language Identification ◽  
Indian Language ◽  
Regular Method ◽  
Speech Database ◽  
Day By Day

Spoken language is the most regular method of correspondence in this day and age. Endeavours to create language recognizable proof frameworks for Indian dialects have been very restricted because of the issue of speaker accessibility and language readability. However, the necessity of SLID is expanding for common and safeguard applications day by day. Feature extraction is a basic and important procedure performed in LID. A sound example is changed over into a spectrogram visual portrayal which describes a range of frequencies in regard with time. Three such spectrogram visuals were generated namely Log Spectrogram, Gammatonegram and IIR-CQT Spectrogram for audio samples from the standardized IIIT-H Indic Speech Database. These visual representations depict language specific details and the nature of each language. These spectrograms images were then used as an input to the CNN. Classification accuracy of 98.86% was obtained using the proposed methodology.

scholarly journals Diagnosis of Alzheimer’s Disease Severity with fMRI Images Using Robust Multitask Feature Extraction Method and Convolutional Neural Network (CNN)

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Morteza Amini ◽  
MirMohsen Pedram ◽  
AliReza Moradi ◽  
Mahshad Ouchani
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Early Stage ◽  
Support Vector ◽  
Feature Extraction Method

The automatic diagnosis of Alzheimer’s disease plays an important role in human health, especially in its early stage. Because it is a neurodegenerative condition, Alzheimer’s disease seems to have a long incubation period. Therefore, it is essential to analyze Alzheimer’s symptoms at different stages. In this paper, the classification is done with several methods of machine learning consisting of K -nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), linear discrimination analysis (LDA), and random forest (RF). Moreover, novel convolutional neural network (CNN) architecture is presented to diagnose Alzheimer’s severity. The relationship between Alzheimer’s patients’ functional magnetic resonance imaging (fMRI) images and their scores on the MMSE is investigated to achieve the aim. The feature extraction is performed based on the robust multitask feature learning algorithm. The severity is also calculated based on the Mini-Mental State Examination score, including low, mild, moderate, and severe categories. Results show that the accuracy of the KNN, SVM, DT, LDA, RF, and presented CNN method is 77.5%, 85.8%, 91.7%, 79.5%, 85.1%, and 96.7%, respectively. Moreover, for the presented CNN architecture, the sensitivity of low, mild, moderate, and severe status of Alzheimer patients is 98.1%, 95.2%,89.0%, and 87.5%, respectively. Based on the findings, the presented CNN architecture classifier outperforms other methods and can diagnose the severity and stages of Alzheimer’s disease with maximum accuracy.

Fusion Process Neural Networks Classifier Oriented Time Series

2019 ◽  
Vol 16 (10) ◽  
pp. 4059-4063
Author(s):  
Ge Li ◽  
Hu Jing ◽  
Chen Guangsheng
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Feature Extraction ◽  
Time Series Data ◽  
Learning Algorithm ◽  
Series Data ◽  
Time Varying ◽  
Synthetic Control ◽  
Classification Feature

Based on the consideration of complementary advantages, different wavelet, fractal and statistical methods are integrated to complete the classification feature extraction of time series. Combined with the advantage of process neural networks that processing time-varying information, we propose a fusion classifier with process neural network oriented time series. Be taking advantage of the multi-fractal processing nonlinear feature of time series data classification, the strong adaptability of the wavelet technique for time series data and the effect of statistical features on the classification of time series data, we can achieve the classification feature extraction of time series. Additionally, using time-varying input characteristics of process neural networks, the pattern matching of timevarying input information and space-time aggregation operation is realized. The feature extraction of time series with the above three methods is fused to the distance calculation between time-varying inputs and cluster space in process neural networks. We provide the process neural network fusion to the learning algorithm and optimize the calculation process of the time series classifier. Finally, we report the performance of our classification method using Synthetic Control Charts data from the UCI dataset and illustrate the advantage and validity of the proposed method.

scholarly journals Multilingual Text Detection with Nonlinear Neural Network

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Lin Li ◽  
Shengsheng Yu ◽  
Luo Zhong ◽  
Xiaozhen Li
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Learning Algorithm ◽  
Feature Learning ◽  
Text Detection ◽  
Natural Scenes ◽  
Nonlinear Network ◽  
Text Feature ◽  
Representational Power ◽  
Multilingual Text

Multilingual text detection in natural scenes is still a challenging task in computer vision. In this paper, we apply an unsupervised learning algorithm to learn language-independent stroke feature and combine unsupervised stroke feature learning and automatically multilayer feature extraction to improve the representational power of text feature. We also develop a novel nonlinear network based on traditional Convolutional Neural Network that is able to detect multilingual text regions in the images. The proposed method is evaluated on standard benchmarks and multilingual dataset and demonstrates improvement over the previous work.

scholarly journals Mechanical Fault Diagnosis Using Color Image Recognition of Vibration Spectrogram Based on Quaternion Invariable Moment

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Liang Hua ◽  
Yujian Qiang ◽  
Juping Gu ◽  
Ling Chen ◽  
Xinsong Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Extraction Method ◽  
Learning Algorithm ◽  
Probabilistic Neural Network ◽  
Invariant Moment ◽  
Feature Extraction Method ◽  
Time Frequency ◽  
Neural Network Algorithm

Automatic extraction of time-frequency spectral image of mechanical faults can be achieved and faults can be identified consequently when rotating machinery spectral image processing technology is applied to fault diagnosis, which is an advantage. Acquired mechanical vibration signals can be converted into color time-frequency spectrum images by the processing of pseudo Wigner-Ville distribution. Then a feature extraction method based on quaternion invariant moment was proposed, combining image processing technology and multiweight neural network technology. The paper adopted quaternion invariant moment feature extraction method and gray level-gradient cooccurrence matrix feature extraction method and combined them with geometric learning algorithm and probabilistic neural network algorithm, respectively, and compared the recognition rates of rolling bearing faults. The experimental results show that the recognition rates of quaternion invariant moment are higher than gray level-gradient cooccurrence matrix in the same recognition method. The recognition rates of geometric learning algorithm are higher than probabilistic neural network algorithm in the same feature extraction method. So the method based on quaternion invariant moment geometric learning and multiweight neural network is superior. What is more, this algorithm has preferable generalization performance under the condition of fewer samples, and it has practical value and acceptation on the field of fault diagnosis for rotating machinery as well.

scholarly journals A Novel Hybrid Path Planning Method Based on Q-Learning and Neural Network for Robot Arm

2021 ◽  
Vol 11 (15) ◽  
pp. 6770
Author(s):  
Ali Abdi ◽  
Dibash Adhikari ◽  
Ju Hong Park
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Learning Algorithm ◽  
Hybrid Approach ◽  
Active Phase ◽  
Planning Method ◽  
Environment Interaction ◽  
Robot Arm ◽  
Q Learning ◽  
Passive Phase

Path planning for robot arms to reach a target and avoid obstacles has had a crucial role in manufacturing automation. Although many path planning algorithms, including RRT, APF, PRM, and RL-based, have been presented, they have many problems: a time-consuming process, high computational costs, slowness, non-optimal paths, irregular paths, failure to find a path, and complexity. Scholars have tried to address some of these issues. However, those methods still suffer from slowness and complexity. In order to address these two limitations, this paper presents a new hybrid path planning method that contains two separate parts: action-finding (active approach) and angle-finding (passive approach). In the active phase, the Q-learning algorithm is used to find a sequence of simple actions, including up, down, left, and right, to reach the target cell in a gridded workspace. In the passive phase, the joints angles of the robot arm, with respect to the found actions, are obtained by the trained neural network. The simulation and test results show that this hybrid approach significantly improves the slowness and complexity due to using the simplified agent-environment interaction in the active phase and simple computing the joints angles in the passive phase.

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