scholarly journals Music recognition algorithm based on T-S cognitive neural network

2019 ◽  
Vol 10 (1) ◽  
pp. 135-140 ◽  
Author(s):  
Fei Yan
Keyword(s):  
Neural Network ◽  
Relevant Information ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Experimental Result ◽  
Function Parameter ◽  
Main Task ◽  
Improved Genetic Algorithm ◽  
Compensation Factor ◽  
Music Recognition

Abstract The main task of music recognition is to acquire relevant information of music content through processing and feature extraction of audio signals, and then used for comparison, classification, and automatic recording. The cognitive neural network based on T-S model is used to train the network weights with improved genetic algorithm in the paper. The strategy of membership function parameter adjustment is combined with the combination of momentum method and learning rate adaptive adjustment. The new proposed algorithm can be used in the music recognition algorithm by adding a compensation factor related to the input dimension on the membership degree, and the experimental result of the rule disaster caused by the excessive input dimension shows that the new proposed method can be applied to the music recognition system. At the same time, it shows that the accuracy rate of the recognition network is more accurate than that of the other algorithms, and its robustness is better.

scholarly journals Automatic Number Plate Recognition System using Connected Component Analysis and Convolutional Neural Network

2021 ◽  
Vol 11 (1) ◽  
pp. 167-173
Author(s):  
Sneh Kanwar Singh ◽  
◽  
Dr. Raman Maini ◽  
Dr. Dhavlessh Ratan ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Technological Progress ◽  
Recognition System ◽  
Experimental Result ◽  
License Plate ◽  
Employee Productivity ◽  
Connected Component ◽  
Principal Function ◽  
Video Frames

Technology is becoming constantly important for customers. Automatic number plate Recognition (ANPR) is a device which enables the identification of a number plate in real time. For an intelligent car service, ANPR helps to promote growth, customize the classic app and increase consumer and employee productivity. Within the specification, the principal function of ANPR lies of removing the characteristics from an illustration of a license plate. An application that enables customers to display automobile repairs through the license platform number only derived from a loaded picture is augmented by a smart car service. Technological progress is that, so it is thought that improvement is important in this region too, so the best choice for automotive services is a smart car company. This work proposed a methodology to detect the numbers from car license plate using convolutional neural network. In the preprocessing of photographs on license plates, the WLS and FFT filters were included. The images are then fed into the convolutional trainings neural network. On more plates and tests is reported during the testing. Therefore, the findings indicate that the proposed solution can be taken in less time from the license model to accurately identify the characters. The experimental result shows the significance of proposed research by achieving an accuracy of 98% for the localization and true recognition of license plates from the video frames.

Deep Learning based Speech Emotion Recognition System

2021 ◽  
Vol 23 (12) ◽  
pp. 212-223
Author(s):  
P Jothi Thilaga ◽  
◽  
S Kavipriya ◽  
K Vijayalakshmi ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Emotion Recognition ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Speech Emotion Recognition ◽  
Natural Interaction ◽  
Everyday Activities ◽  
Verbal Content ◽  
Voice Interaction

Emotions are elementary for humans, impacting perception and everyday activities like communication, learning and decision-making. Speech emotion Recognition (SER) systems aim to facilitate the natural interaction with machines by direct voice interaction rather than exploitation ancient devices as input to know verbal content and build it straightforward for human listeners to react. During this SER system primarily composed of 2 sections called feature extraction and feature classification phase. SER implements on bots to speak with humans during a non-lexical manner. The speech emotion recognition algorithm here is predicated on the Convolutional Neural Network (CNN) model, which uses varied modules for emotion recognition and classifiers to differentiate feelings like happiness, calm, anger, neutral state, sadness, and fear. The accomplishment of classification is predicated on extracted features. Finally, the emotion of a speech signal will be determined.

scholarly journals A new optical music recognition system based on combined neural network

2015 ◽  
Vol 58 ◽  
pp. 1-7 ◽  
Author(s):  
Cuihong Wen ◽  
Ana Rebelo ◽  
Jing Zhang ◽  
Jaime Cardoso
Keyword(s):  
Neural Network ◽  
Recognition System ◽  
Optical Music Recognition ◽  
Music Recognition

scholarly journals Neural Architecture Search for Lightweight Neural Network in Food Recognition

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1245
Author(s):  
Ren Zhang Tan ◽  
XinYing Chew ◽  
Khai Wah Khaw
Keyword(s):  
Neural Network ◽  
Model Performance ◽  
Essential Element ◽  
Neural Model ◽  
Search Space ◽  
Recognition System ◽  
Experimental Result ◽  
Neural Architecture ◽  
Balanced Diet ◽  
Food Recognition

Healthy eating is an essential element to prevent obesity that will lead to chronic diseases. Despite numerous efforts to promote the awareness of healthy food consumption, the obesity rate has been increased in the past few years. An automated food recognition system is needed to serve as a fundamental source of information for promoting a balanced diet and assisting users to understand their meal consumption. In this paper, we propose a novel Lightweight Neural Architecture Search (LNAS) model to self-generate a thin Convolutional Neural Network (CNN) that can be executed on mobile devices with limited processing power. LNAS has a sophisticated search space and modern search strategy to design a child model with reinforcement learning. Extensive experiments have been conducted to evaluate the model generated by LNAS, namely LNAS-NET. The experimental result shows that the proposed LNAS-NET outperformed the state-of-the-art lightweight models in terms of training speed and accuracy metric. Those experiments indicate the effectiveness of LNAS without sacrificing the model performance. It provides a good direction to move toward the era of AutoML and mobile-friendly neural model design.

Development of a 3D Viewpoint Recognition System Using a Neural Network for Autonomous Flying Drones

2016 ◽  
Vol 136 (10) ◽  
pp. 719-726
Author(s):  
Junya Arakaki ◽  
Hitoshi Ishikawa ◽  
Itaru Nagayama
Keyword(s):  
Neural Network ◽  
Recognition System

Analysis of Fingerprint Recognition System Using Neural Network

2020 ◽  
Author(s):  
Ganesh Awasthi ◽  
Dr. Hanumant Fadewar ◽  
Almas Siddiqui ◽  
Bharatratna P. Gaikwad
Keyword(s):  
Neural Network ◽  
Recognition System ◽  
Fingerprint Recognition

Automatic Car Number Plate Recognition System for Authorization

2017 ◽  
Vol MCSP2017 (01) ◽  
pp. 30-34
Author(s):  
Somalin Sandha ◽  
Debaraj Rana
Keyword(s):  
Neural Network ◽  
Recognition System ◽  
Performance Level ◽  
License Plate ◽  
License Plate Recognition ◽  
Human Power ◽  
Automatic License Plate Recognition

In present day scenario the security and authentication is very much needed to make a safety world. Beside all security one vital issue is recognition of number plate from the car for Authorization. In the busy world everything cannot be monitor by a human, so automatic license plate recognition is one of the best application for authorization without involvement of human power. In the proposed method we have make the problem into three fold, firstly extraction of number plate region, secondly segmentation of character and finally Authorization through recognition and classification. For number plate extraction and segmentation we have used morphological based approaches where as for classification we have used Neural Network as classifier. The proposed method is working well in varieties of scenario and the performance level is quiet good.

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