CNN: A SPEAKER RECOGNITION SYSTEM USING A CASCADED NEURAL NETWORK

1996 ◽  
Vol 07 (02) ◽  
pp. 203-212 ◽  
Author(s):  
M. ZAKI ◽  
A. GHALWASH ◽  
A.A. ELKOUNY
Keyword(s):  
Neural Network ◽  
Speaker Recognition ◽  
Network Models ◽  
Recognition System ◽  
Conventional Technique ◽  
Neural Network Models ◽  
Neural Network Approach ◽  
Unsupervised Neural Network ◽  
And Performance ◽  
Filtration Stage

The main emphasis of this paper is to present an approach for combining supervised and unsupervised neural network models to the issue of speaker recognition. To enhance the overall operation and performance of recognition, the proposed strategy integrates the two techniques, forming one global model called the cascaded model. We first present a simple conventional technique based on the distance measured between a test vector and a reference vector for different speakers in the population. This particular distance metric has the property of weighting down the components in those directions along which the intraspeaker variance is large. The reason for presenting this method is to clarify the discrepancy in performance between the conventional and neural network approach. We then introduce the idea of using unsupervised learning technique, presented by the winner-take-all model, as a means of recognition. Due to several tests that have been conducted and in order to enhance the performance of this model, dealing with noisy patterns, we have preceded it with a supervised learning model—the pattern association model—which acts as a filtration stage. This work includes both the design and implementation of both conventional and neural network approaches to recognize the speakers templates—which are introduced to the system via a voice master card and preprocessed before extracting the features used in the recognition. The conclusion indicates that the system performance in case of neural network is better than that of the conventional one, achieving a smooth degradation in respect of noisy patterns, and higher performance in respect of noise-free patterns.

Comparison of three unsupervised neural network models for first Painlevé Transcendent

2014 ◽  
Vol 26 (5) ◽  
pp. 1055-1071 ◽  
Author(s):  
Muhammad Asif Zahoor Raja ◽  
Junaid Ali Khan ◽  
Syed Muslim Shah ◽  
Raza Samar ◽  
Djilali Behloul
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Painlevé Transcendent ◽  
Unsupervised Neural Network

scholarly journals Unsupervised neural network models of the ventral visual stream

2021 ◽  
Vol 118 (3) ◽  
pp. e2014196118
Author(s):  
Chengxu Zhuang ◽  
Siming Yan ◽  
Aran Nayebi ◽  
Martin Schrimpf ◽  
Michael C. Frank ◽  
...  
Keyword(s):  
Neural Network ◽  
Unsupervised Learning ◽  
Network Models ◽  
Quantitative Model ◽  
Ventral Stream ◽  
Neural Network Models ◽  
Visual Stream ◽  
Unsupervised Neural Network ◽  
Supervised Methods ◽  
Ventral Visual Stream

Deep neural networks currently provide the best quantitative models of the response patterns of neurons throughout the primate ventral visual stream. However, such networks have remained implausible as a model of the development of the ventral stream, in part because they are trained with supervised methods requiring many more labels than are accessible to infants during development. Here, we report that recent rapid progress in unsupervised learning has largely closed this gap. We find that neural network models learned with deep unsupervised contrastive embedding methods achieve neural prediction accuracy in multiple ventral visual cortical areas that equals or exceeds that of models derived using today’s best supervised methods and that the mapping of these neural network models’ hidden layers is neuroanatomically consistent across the ventral stream. Strikingly, we find that these methods produce brain-like representations even when trained solely with real human child developmental data collected from head-mounted cameras, despite the fact that these datasets are noisy and limited. We also find that semisupervised deep contrastive embeddings can leverage small numbers of labeled examples to produce representations with substantially improved error-pattern consistency to human behavior. Taken together, these results illustrate a use of unsupervised learning to provide a quantitative model of a multiarea cortical brain system and present a strong candidate for a biologically plausible computational theory of primate sensory learning.

scholarly journals Prediction of Area and Production of Groundnut Using Box-Jenkins Arima and Neural Network Approach

2020 ◽  
Author(s):  
S. T. Pavana Kumar ◽  
Ferdinand B. Lyngdoh
Keyword(s):  
Neural Network ◽  
Moving Average ◽  
Arima Model ◽  
Network Models ◽  
Arima Models ◽  
Neural Network Models ◽  
Neural Network Approach ◽  
Data Set ◽  
The Neural Network ◽  
Hidden Layer

Selection of parameters for Auto Regressive Integrated Moving Average (ARIMA) model in the prediction process is one of the most important tasks. In the present study, groundnut data was utlised to decide appropriate p, d, q parameters for ARIMA model for the prediction purpose. Firstly, the models were fit to data without splitting into training and validation/testing sets and evaluated for their efficiency in predicting the area and production of groundnut over the years. Meanwhile, models are compared among other fitted ARIMA models with different p, d, q parameters based on decision criteria’s viz., ME, RMSE, MAPE, AIC, BIC and R-Square. The ARIMA model with parameters p-2 d-1-2, q-1-2 are found adequate in predicting the area as well as production of groundnut. The model ARIMA (2, 2, 2) and ARIMA (2,1,1) predicted the area of groundnut crop with minimum error estimates and residual characteristics (ei). The models were fit into split data i.e., training and test data set, but these models’ prediction power (R-Square) declined during testing. In case of predicting the area, ARIMA (2,2,2) was consistent over the split data but it was not consistent while predicting the production over years. Feed-forward neural networks with single hidden layer were fit to complete, training and split data. The neural network models provided better estimates compared to Box-Jenkins ARIMA models. The data was analysed using R-Studio.

scholarly journals Plant Disease Detection and Classification using CNN

2021 ◽  
Vol 10 (3) ◽  
pp. 152-156
Author(s):  
Rinu R ◽  
◽  
Manjula S H ◽  
Keyword(s):  
Neural Network ◽  
Economic Status ◽  
Network Models ◽  
Training Model ◽  
Input Image ◽  
Plant Diseases ◽  
Human Beings ◽  
Neural Network Models ◽  
Neural Network Approach

Agriculture is one field which has a high impact on life and economic status of human beings. Improper management leads to loss in agricultural products. Diseases are detrimental to the plant’s health which in turn affects its growth. To ensure minimal loss to the cultivated crop, it is crucial to supervise its growth. Convolutional Neural Network is a class of Deep learning used majorly for image classification, other mainstream tasks such as image segmentation and signal processing. The main aim of the proposed work is to find a solution to the problem of 38 different classes of plant diseases detection using the simplest approach while making use of minimal computing resources to achieve better results compared to the traditional models. VGG16 training model is deployed for detection and classification of plant diseases. Neural network models employ automatic feature extraction to aid in the classification of the input image into respective disease classes. This proposed system has achieved an average accuracy of 94.8% indicating the feasibility of the neural network approach even under unfavorable conditions.

scholarly journals Unsupervised Neural Network Models of the Ventral Visual Stream

2020 ◽  
Author(s):  
Chengxu Zhuang ◽  
Siming Yan ◽  
Aran Nayebi ◽  
Martin Schrimpf ◽  
Michael C. Frank ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Ventral Stream ◽  
Visual Development ◽  
Neural Network Models ◽  
Visual Stream ◽  
Unsupervised Neural Network ◽  
Supervised Methods ◽  
Ventral Visual Stream ◽  
Visual Cortical

Deep neural networks currently provide the best quantitative models of the response patterns of neurons throughout the primate ventral visual stream. However, such networks have remained implausible as a model of the development of the ventral stream, in part because they are trained with supervised methods requiring many more labels than are accessible to infants during development. Here, we report that recent rapid progress in unsupervised learning has largely closed this gap. We find that neural network models learned with deep unsupervised contrastive embedding methods achieve neural prediction accuracy in multiple ventral visual cortical areas that equals or exceeds that of models derived using today’s best supervised methods, and that the mapping of these neural network models’ hidden layers is neuroanatomically consistent across the ventral stream. Moreover, we find that these methods produce brain-like representations even when trained on noisy and limited data measured from real children’s developmental experience. We also find that semi-supervised deep contrastive embeddings can leverage small numbers of labelled examples to produce representations with substantially improved error-pattern consistency to human behavior. Taken together, these results suggest that deep contrastive embedding objectives may be a biologically-plausible computational theory of primate visual development.

scholarly journals Speech Command Recognition using Artificial Neural Networks

2020 ◽  
Vol 4 (2) ◽  
pp. 73
Author(s):  
Sushan Poudel ◽  
Dr. R Anuradha
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Convolutional Neural Network ◽  
Recurrent Neural Network ◽  
Network Models ◽  
Recognition System ◽  
Neural Network Models ◽  
Artificial Neural

Speech is one of the most effective way for human and machine to interact. This project aims to build Speech Command Recognition System that is capable of predicting the predefined speech commands. Dataset provided by Google’s TensorFlow and AIY teams is used to implement different Neural Network models which include Convolutional Neural Network and Recurrent Neural Network combined with Convolutional Neural Network. The combination of Convolutional and Recurrent Neural Network outperforms Convolutional Neural Network alone by 8% and achieved 96.66% accuracy for 20 labels.

scholarly journals Deep Neural Network Approach for Prediction of Heating Energy Consumption in Old Houses

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 122
Author(s):  
Sungjin Lee ◽  
Soo Cho ◽  
Seo-Hoon Kim ◽  
Jonghun Kim ◽  
Suyong Chae ◽  
...  
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Low Income ◽  
Prediction Accuracy ◽  
Deep Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Neural Network Approach ◽  
Input Variables ◽  
Heating Energy Consumption

Neural network models are data-driven and are effective for predicting and interpreting nonlinear or unexplainable physical phenomena. This study collected building information and heating energy consumption data from 16,158 old houses, selected key input variables that affect the heating energy consumption based on the collected datasets, and developed a deep neural network (DNN) model that showed the highest accuracy for the prediction of heating energy consumption in an old house. As a result, 11 key input variables were selected, and an optimal DNN model was developed. This optimal DNN model showed the highest prediction accuracy (R2 = 0.961) when the number of hidden layers was five and the number of neurons was 22. When the optimal DNN model was applied for the standard model of low-income detached houses, the prediction accuracy (Cv(RMSE)) of the optimal DNN model, compared to the EnergyPlus calculation result, was 8.74%, which satisfied the ASHRAE standard sufficiently.

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