scholarly journals Genre Classification of Traditional Malay Music Using Spectrogram Correlation

2018 ◽  
Vol 7 (4.11) ◽  
pp. 29
Author(s):  
S. A. Samad ◽  
A. B. Huddin
Keyword(s):  
Template Matching ◽  
Correlation Energy ◽  
Recognition Rate ◽  
Salient Feature ◽  
Average Correlation ◽  
Genre Classification ◽  
Average Recognition Rate ◽  
Number Of Segments ◽  
Unconstrained Minimum

A method to classify the genre of traditional Malay music using spectrogram correlation is described.  The method can be divided into three distinct parts consisting of spectrogram construction that retains the most salient feature of the music, template construction that takes into account the variations in music within a genre as well as the music progresses, and template matching based on spectrogram image cross-correlation with unconstrained minimum average correlation energy filters. Experiments conducted with seven genres of traditional Malay music show that the recognition accuracy is dependent on the number of segments used to construct the filter templates, which in turn is related to the length of music segment used. Despite using a small dataset, an average recognition rate of 61.8 percent was obtained for music segments lasting 180 seconds using six relatively short excerpts.  

scholarly journals Anomaly Detection in Electroencephalogram Signals Using Unconstrained Minimum Average Correlation Energy Filter

2009 ◽  
Vol 5 (7) ◽  
pp. 501-506 ◽  
Author(s):  
Aini Hussain ◽  
Rosniwati Ghafar ◽  
Salina Abdul Samad ◽  
Nooritawati Md Tahir
Keyword(s):  
Anomaly Detection ◽  
Correlation Energy ◽  
Average Correlation ◽  
Unconstrained Minimum

scholarly journals Electromyographic Grasp Recognition for a Five Fingered Robotic Hand

2012 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Nayan M. Kakoty ◽  
Mantoo Kaiborta ◽  
Shyamanta M. Hazarika
Keyword(s):  
Daily Living ◽  
Wavelet Decomposition ◽  
Recognition Rate ◽  
Support Vector ◽  
Robotic Hand ◽  
Radial Basis Function Kernel ◽  
Kernel Support Vector Machine ◽  
Average Recognition Rate ◽  
Electromyographic Signals

This paper presents classification of grasp types based on surface electromyographic signals. Classification is through radial basis function kernel support vector machine using sum of wavelet decomposition coefficients of the EMG signals. In a study involving six subjects, we achieved an average recognition rate of 86%. The electromyographic grasp recognition together with a 8-bit microcontroller has been employed to control a five<br />fingered robotic hand to emulate six grasp types used during 70% daily living activities.<br /><br />

scholarly journals EEG-Based Emotion Recognition by Exploiting Fused Network Entropy Measures of Complex Networks across Subjects

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 984
Author(s):  
Longxin Yao ◽  
Mingjiang Wang ◽  
Yun Lu ◽  
Heng Li ◽  
Xue Zhang
Keyword(s):  
Complex Networks ◽  
Emotion Recognition ◽  
Recognition Rate ◽  
Clustering Coefficient ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Average Recognition Rate ◽  
Entropy Measures ◽  
Network Entropy

It is well known that there may be significant individual differences in physiological signal patterns for emotional responses. Emotion recognition based on electroencephalogram (EEG) signals is still a challenging task in the context of developing an individual-independent recognition method. In our paper, from the perspective of spatial topology and temporal information of brain emotional patterns in an EEG, we exploit complex networks to characterize EEG signals to effectively extract EEG information for emotion recognition. First, we exploit visibility graphs to construct complex networks from EEG signals. Then, two kinds of network entropy measures (nodal degree entropy and clustering coefficient entropy) are calculated. By applying the AUC method, the effective features are input into the SVM classifier to perform emotion recognition across subjects. The experiment results showed that, for the EEG signals of 62 channels, the features of 18 channels selected by AUC were significant (p < 0.005). For the classification of positive and negative emotions, the average recognition rate was 87.26%; for the classification of positive, negative, and neutral emotions, the average recognition rate was 68.44%. Our method improves mean accuracy by an average of 2.28% compared with other existing methods. Our results fully demonstrate that a more accurate recognition of emotional EEG signals can be achieved relative to the available relevant studies, indicating that our method can provide more generalizability in practical use.

scholarly journals Practical Recognition System for Text Printed on Clear Reflected Material

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Khader Mohammad ◽  
Sos Agaian
Keyword(s):  
Processing Time ◽  
Recognition Rate ◽  
Recognition System ◽  
Experimental Results ◽  
Research Fields ◽  
Average Recognition Rate ◽  
Clear Plastic ◽  
Novel Methods ◽  
Processor Speed ◽  
Background Contrast

Text embedded in an image contains useful information for applications in the medical, industrial, commercial, and research fields. While many systems have been designed to correctly identify text in images, no work addressing the recognition of degraded text on clear plastic has been found. This paper posits novel methods and an apparatus for extracting text from an image with the practical assumption: (a) poor background contrast, (b) white, curved, and/or differing fonts or character width between sets of images, (c) dotted text printed on curved reflective material, and/or (d) touching characters. Methods were evaluated using a total of 100 unique test images containing a variety of texts captured from water bottles. These tests averaged a processing time of ~10 seconds (using MATLAB R2008A on an HP 8510 W with 4 G of RAM and 2.3 GHz of processor speed), and experimental results yielded an average recognition rate of 90 to 93% using customized systems generated by the proposed development.

scholarly journals Genre of Bangla Music: A Machine Classification Learning Approach

2019 ◽  
Vol 18 (2) ◽  
pp. 66-72
Author(s):  
Abhijit Bhowmik ◽  
AZM Ehtesham Chowdhury
Keyword(s):  
Classification Systems ◽  
Learning Approach ◽  
Large Set ◽  
Automated Classification ◽  
Classification Learning ◽  
Genre Classification ◽  
Machine Learning Approach ◽  
Music Databases ◽  
East Asian Language

The necessity for designing autonomous indexing tools to establish expressive and efficient means of describing musical media content is well recognized. Music genre classification systems are significant to manage and use music databases. This research paper proposes an enhanced method to automatically classify music into different genre using a machine learning approach and presents the insight and results of the application of the proposed scheme to the classification of a large set of The Bangla music content, a South-East Asian language rich with a variety of music genres developed over many centuries. Building upon musical feature extraction and decision-making techniques, we propose new features and procedures to achieve enhanced accuracy. We demonstrate the efficacy of the proposed method by extracting features from a dataset of hundreds of The Bangla music pieces and testing the automatic classification decisions. This is the first development of an automated classification technique applied specifically to the Bangla music to the best of our knowledge, while the superior accuracy of the method makes it universally applicable.

scholarly journals Performance Evaluation of Automatic Number Plate Recognition on Android Smartphone Platform

2017 ◽  
Vol 7 (4) ◽  
pp. 1973
Author(s):  
Teddy Surya Gunawan ◽  
Abdul Mutholib ◽  
Mira Kartiwi
Keyword(s):  
Character Recognition ◽  
Template Matching ◽  
Optical Character Recognition ◽  
Processing Time ◽  
Intelligent System ◽  
Recognition Rate ◽  
The Other ◽  
Other Hand ◽  
Additional Processing ◽  
Artificial Neural Network Ann

<span>Automatic Number Plate Recognition (ANPR) is an intelligent system which has the capability to recognize the character on vehicle number plate. Previous researches implemented ANPR system on personal computer (PC) with high resolution camera and high computational capability. On the other hand, not many researches have been conducted on the design and implementation of ANPR in smartphone platforms which has limited camera resolution and processing speed. In this paper, various steps to optimize ANPR, including pre-processing, segmentation, and optical character recognition (OCR) using artificial neural network (ANN) and template matching, were described. The proposed ANPR algorithm was based on Tesseract and Leptonica libraries. For comparison purpose, the template matching based OCR will be compared to ANN based OCR. Performance of the proposed algorithm was evaluated on the developed Malaysian number plates’ image database captured by smartphone’s camera. Results showed that the accuracy and processing time of the proposed algorithm using template matching was 97.5% and 1.13 seconds, respectively. On the other hand, the traditional algorithm using template matching only obtained 83.7% recognition rate with 0.98 second processing time. It shows that our proposed ANPR algorithm improved the recognition rate with negligible additional processing time.</span>

scholarly journals 3D Robot Vision System through 2D Shape Based Matching Using Gaussian Smoothing for Gluing Application

2018 ◽  
Vol 7 (4.33) ◽  
pp. 487
Author(s):  
Mohamad Haniff Harun ◽  
Mohd Shahrieel Mohd Aras ◽  
Mohd Firdaus Mohd Ab Halim ◽  
Khalil Azha Mohd Annuar ◽  
Arman Hadi Azahar ◽  
...  
Keyword(s):  
Template Matching ◽  
Shape Matching ◽  
Vision System ◽  
Recognition Rate ◽  
Three Dimensional ◽  
Robot Vision ◽  
Region Of Interest ◽  
Gaussian Smoothing ◽  
Detection Approach ◽  
Vision Algorithm

This investigation is solely on the adaptation of a vision system algorithm to classify the processes to regulate the decision making related to the tasks and defect’s recognition. These idea stresses on the new method on vision algorithm which is focusing on the shape matching properties to classify defects occur on the product. The problem faced before that the system required to process broad data acquired from the object caused the time and efficiency slightly decrease. The propose defect detection approach combine with Region of Interest, Gaussian smoothing, Correlation and Template Matching are introduced. This application provides high computational savings and results in better recognition rate about 95.14%. The defects occur provides with information of the height which corresponds by the z-coordinate, length which corresponds by the y-coordinate and width which corresponds by the x-coordinate. This data gathered from the proposed system using dual camera for executing the three dimensional transformation.  

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