Wayang Robot with Gamelan Music Pattern Recognition

2017 ◽  
Vol 29 (1) ◽  
pp. 137-145 ◽  
Author(s):  
Tito Pradhono Tomo ◽  
◽  
Alexander Schmitz ◽  
Guillermo Enriquez ◽  
Shuji Hashimoto ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Degrees Of Freedom ◽  
Recognition Rate ◽  
Support Vector ◽  
Svm Classifier ◽  
Intangible Cultural Heritage ◽  
Feed Forward Neural Network ◽  
Mel Frequency Cepstral Coefficients ◽  
Puppet Theater ◽  
Music Patterns

[abstFig src='/00290001/13.jpg' width='245' text='Wayang robot' ] This paper proposes a way to protect endangered wayang puppet theater, an intangible cultural heritage from Indonesia, by turning a robot into a puppeteer successor. We developed a seven degrees-of-freedom (DOF) manipulator to actuate the sticks attached to the wayang puppet body and hands. The robot can imitate 8 distinct human puppeteer’s manipulations. Furthermore, we developed a gamelan music pattern recognition, towards a robot that can perform based on the gamelan music. In the offline experiment, we extracted energy (time domain), spectral rolloff, 13 Mel-frequency cepstral coefficients (MFCCs), and the harmonic ratio from 5 s long clips, every 0.025 s, with a window length of 1 s, for a total of 2576 features. Two classifiers (3 layers feed-forward neural network (FNN) and multi-class Support Vector Machine (SVM)) were compared. The SVM classifier outperformed the FNN classifier with a recognition rate of 96.4% for identifying the three different gamelan music patterns.

Pattern recognition of head movement based on mechanomyography and its application

2020 ◽  
Vol 65 (1) ◽  
pp. 51-60
Author(s):  
Xiaolin Gu ◽  
Qing Wu ◽  
Yue Zhang ◽  
Hao Zhong ◽  
Shengli Zhang ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Wavelet Packet ◽  
Recognition Rate ◽  
Head Movements ◽  
Support Vector ◽  
Svm Classifier ◽  
Principal Diagonal ◽  
Linear Discriminant ◽  
Single Action ◽  
Action Frame

AbstractThe first part of this study investigated pattern recognition of head movements based on mechanomyography (MMG) signals. Four channel MMG signals were collected from the sternocleidomastoid (SCM) muscles and the splenius capitis (SPL) muscles in the subjects’ neck when they bowed the head, raised the head, side-bent to left, side-bent to right, turned to left and turned to right. The MMG signals were then filtered, normalized and divided using an unequal length segmentation algorithm into a single action frame. After extracting the energy features of the wavelet packet coefficients and the feature of the principal diagonal slices of the bispectrum, the dimension of the energy features were reduced by the Fisher linear discriminant analysis (FLDA). Finally, all the features were classified through the support vector machine (SVM) classifier. The recognition rate was up to 95.92%. On this basis, the second part of this study used the head movements to control a car model for simulating the control of a wheelchair, and the success rate was 85.74%.

AUTOMATED RECOGNITION OF SINGLE & HYBRID POWER QUALITY DISTURBANCES USING WAVELET TRANSFORM BASED SUPPORT VECTOR MACHINE

2016 ◽  
Vol 79 (1) ◽  
Author(s):  
Suhail Khokhar ◽  
A. A. Mohd Zin ◽  
M. A. Bhayo ◽  
A. S. Mokhtar
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Wavelet Transform ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Recognition Rate ◽  
Classification Performance ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Feed Forward Neural Network

The monitoring of power quality (PQ) disturbances in a systematic and automated way is an important issue to prevent detrimental effects on power system. The development of new methods for the automatic recognition of single and hybrid PQ disturbances is at present a major concern. This paper presents a combined approach of wavelet transform based support vector machine (WT-SVM) for the automatic classification of single and hybrid PQ disturbances. The proposed approach is applied by using synthetic models of various single and hybrid PQ signals. The suitable features of the PQ waveforms were first extracted by using discrete wavelet transform. Then SVM classifies the type of PQ disturbances based on these features. The classification performance of the proposed algorithm is also compared with wavelet based radial basis function neural network, probabilistic neural network and feed-forward neural network. The experimental results show that the recognition rate of the proposed WT-SVM based classification system is more accurate and much better than the other classifiers. 

scholarly journals Persian Handwritten Digit Recognition Using Combination of Convolutional Neural Network and Support Vector Machine Methods

2020 ◽  
Vol 17 (4) ◽  
pp. 572-578
Author(s):  
Mohammad Parseh ◽  
Mohammad Rahmanimanesh ◽  
Parviz Keshavarzi
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Recognition Rate ◽  
Support Vector ◽  
Svm Classifier ◽  
Handwritten Digit Recognition ◽  
Digit Recognition ◽  
Handwritten Digit

Persian handwritten digit recognition is one of the important topics of image processing which significantly considered by researchers due to its many applications. The most important challenges in Persian handwritten digit recognition is the existence of various patterns in Persian digit writing that makes the feature extraction step to be more complicated.Since the handcraft feature extraction methods are complicated processes and their performance level are not stable, most of the recent studies have concentrated on proposing a suitable method for automatic feature extraction. In this paper, an automatic method based on machine learning is proposed for high-level feature extraction from Persian digit images by using Convolutional Neural Network (CNN). After that, a non-linear multi-class Support Vector Machine (SVM) classifier is used for data classification instead of fully connected layer in final layer of CNN. The proposed method has been applied to HODA dataset and obtained 99.56% of recognition rate. Experimental results are comparable with previous state-of-the-art methods

HEART SOUND CLASSIFICATION AND RECOGNITION BASED ON EEMD AND CORRELATION DIMENSION

2014 ◽  
Vol 14 (04) ◽  
pp. 1450046 ◽  
Author(s):  
WENYING ZHANG ◽  
XINGMING GUO ◽  
ZHIHUI YUAN ◽  
XINGHUA ZHU
Keyword(s):  
Feature Extraction ◽  
Heart Sound ◽  
Linear Time ◽  
Heart Diseases ◽  
Recognition Rate ◽  
Heart Sounds ◽  
Support Vector ◽  
Svm Classifier ◽  
Intrinsic Mode Functions ◽  
Sound Classification

Analysis of heart sound is of great importance to the diagnosis of heart diseases. Most of the feature extraction methods about heart sound have focused on linear time-variant or time-invariant models. While heart sound is a kind of highly nonstationary and nonlinear vibration signal, traditional methods cannot fully reveal its essential properties. In this paper, a novel feature extraction approach is proposed for heart sound classification and recognition. The ensemble empirical mode decomposition (EEMD) method is used to decompose the heart sound into a finite number of intrinsic mode functions (IMFs), and the correlation dimensions of the main IMF components (IMF1~IMF4) are calculated as feature set. Then the classical Binary Tree Support Vector Machine (BT-SVM) classifier is employed to classify the heart sounds which include the normal heart sounds (NHSs) and three kinds of abnormal signals namely mitral stenosis (MT), ventricular septal defect (VSD) and aortic stenosis (AS). Finally, the performance of the new feature set is compared with the correlation dimensions of original signals and the main IMF components obtained by the EMD method. The results showed that, for NHSs, the feature set proposed in this paper performed the best with recognition rate of 98.67%. For the abnormal signals, the best recognition rate of 91.67% was obtained. Therefore, the proposed feature set is more superior to two comparative feature sets, which has potential application in the diagnosis of cardiovascular diseases.

scholarly journals A Novel Algorithm for Feature Level Fusion Using SVM Classifier for Multibiometrics-Based Person Identification

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Ujwalla Gawande ◽  
Mukesh Zaveri ◽  
Avichal Kapur
Keyword(s):  
Learning Algorithm ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Rejection Rate ◽  
Support Vector ◽  
Svm Classifier ◽  
Fusion Algorithm ◽  
Biometric Systems ◽  
Feature Level Fusion ◽  
Level Fusion

Recent times witnessed many advancements in the field of biometric and ultimodal biometric fields. This is typically observed in the area, of security, privacy, and forensics. Even for the best of unimodal biometric systems, it is often not possible to achieve a higher recognition rate. Multimodal biometric systems overcome various limitations of unimodal biometric systems, such as nonuniversality, lower false acceptance, and higher genuine acceptance rates. More reliable recognition performance is achievable as multiple pieces of evidence of the same identity are available. The work presented in this paper is focused on multimodal biometric system using fingerprint and iris. Distinct textual features of the iris and fingerprint are extracted using the Haar wavelet-based technique. A novel feature level fusion algorithm is developed to combine these unimodal features using the Mahalanobis distance technique. A support-vector-machine-based learning algorithm is used to train the system using the feature extracted. The performance of the proposed algorithms is validated and compared with other algorithms using the CASIA iris database and real fingerprint database. From the simulation results, it is evident that our algorithm has higher recognition rate and very less false rejection rate compared to existing approaches.

scholarly journals RESEARCH ON CROP INFORMATION EXTRACTION OF AGRICULTURAL UAV IMAGES BASED ON BLIND IMAGE DEBLURRING TECHNOLOGY AND SVM

2021 ◽  
pp. 33-42
Author(s):  
Zehai Xu ◽  
Haiyan Song ◽  
Zhiming Wu ◽  
Zefu Xu ◽  
Shifang Wang
Keyword(s):  
Information Extraction ◽  
Recognition Rate ◽  
Objective Evaluation ◽  
Kernel Functions ◽  
The Other ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Svm Classifier ◽  
Evaluation Indexes ◽  
Crop Classification

The blurring of crop images acquired by agricultural Unmanned Aerial Vehicle (UAV) due to sudden inputs by operators, atmospheric disturbance, and many other factors will eventually affect the subsequent crop identification, information extraction, and yield estimation. Aiming at the above problems, the new proposed combined deblurring algorithm based on the re-weighted graph total variation (RGTV) and L0-regularized prior, and the other two representative deblurring algorithms were applied to restore blurry crop images acquired during UAV flight, respectively. The restoration performance was measured by subjective vision, and objective evaluation indexes. The crop shape-related and texture-related feature parameters were then extracted, the Support Vector Machine (SVM) classifier with four common kernel functions was implemented for crop classification to realize the purpose of crop information extraction. The deblurring results showed that the proposed algorithm performed better in suppressing the ringing effect and preserving the image fine details, and retained higher objective evaluation indexes than the other two deblurring algorithms. The comparative analysis of different classification kernel functions showed that the Polynomial kernel function with an average recognition rate of 94.83% was most suitable for crop classification and recognition. The research will help in further popularization of crop monitoring based on UAV low-altitude remote sensing.

scholarly journals Smart Face Detection and Recognition in Low Resolution Images Using Alexnet CNN Compare Accuracy with SVM

2021 ◽  
Vol 36 (1) ◽  
pp. 721-726
Author(s):  
S. Mahesh ◽  
Dr.G. Ramkumar
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Recognition Rate ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Svm Classifier ◽  
Low Resolution ◽  
Accuracy Ratio ◽  
Low Resolution Images

Aim: Machine learning algorithm plays a vital role in various biometric applications due to its admirable result in detection, recognition and classification. The main objective of this work is to perform comparative analysis on two different machine learning algorithms to recognize the person from low resolution images with high accuracy. Materials & Methods: AlexNet Convolutional Neural Network (ACNN) and Support Vector Machine (SVM) classifiers are implemented to recognize the face in a low resolution image dataset with 20 samples each. Results: Simulation result shows that ACNN achieves a significant recognition rate with 98% accuracy over SVM (89%). Attained significant accuracy ratio (p=0.002) in SPSS statistical analysis as well. Conclusion: For the considered low resolution images ACNN classifier provides better accuracy than SVM Classifier.

scholarly journals A Feature Extraction Method of Ship-Radiated Noise Based on Fluctuation-Based Dispersion Entropy and Intrinsic Time-Scale Decomposition

Entropy ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. 693 ◽  
Author(s):  
Zhaoxi Li ◽  
Yaan Li ◽  
Kai Zhang
Keyword(s):  
Feature Extraction ◽  
Time Scale ◽  
Recognition Rate ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Extraction Method ◽  
Radiated Noise ◽  
Intrinsic Time ◽  
Different Types ◽  
Time Scale Decomposition

To improve the feature extraction of ship-radiated noise in a complex ocean environment, fluctuation-based dispersion entropy is used to extract the features of ten types of ship-radiated noise. Since fluctuation-based dispersion entropy only analyzes the ship-radiated noise signal in single scale and it cannot distinguish different types of ship-radiated noise effectively, a new method of ship-radiated noise feature extraction is proposed based on fluctuation-based dispersion entropy (FDispEn) and intrinsic time-scale decomposition (ITD). Firstly, ten types of ship-radiated noise signals are decomposed into a series of proper rotation components (PRCs) by ITD, and the FDispEn of each PRC is calculated. Then, the correlation between each PRC and the original signal are calculated, and the FDispEn of each PRC is analyzed to select the Max-relative PRC fluctuation-based dispersion entropy as the feature parameter. Finally, by comparing the Max-relative PRC fluctuation-based dispersion entropy of a certain number of the above ten types of ship-radiated noise signals with FDispEn, it is discovered that the Max-relative PRC fluctuation-based dispersion entropy is at the same level for similar ship-radiated noise, but is distinct for different types of ship-radiated noise. The Max-relative PRC fluctuation-based dispersion entropy as the feature vector is sent into the support vector machine (SVM) classifier to classify and recognize ten types of ship-radiated noise. The experimental results demonstrate that the recognition rate of the proposed method reaches 95.8763%. Consequently, the proposed method can effectively achieve the classification of ship-radiated noise.

Secured Finger Vein Authentication System Using Contourlet Transform and SVM

2014 ◽  
Vol 573 ◽  
pp. 465-470
Author(s):  
C. Murukesh ◽  
T.K. Nadesh ◽  
K. Thanushkodi
Keyword(s):  
Recognition Rate ◽  
High Accuracy ◽  
Vital Role ◽  
Security And Privacy ◽  
Contourlet Transform ◽  
Support Vector ◽  
Svm Classifier ◽  
Finger Vein ◽  
Authentication System ◽  
Contemporary Approach

Biometrics authentication is playing a vital role in providing security and privacy. This paper presents a contemporary approach for identifying an individual using unimodal biometrics. Finger vein based authentication system is a promising technology and now-a-days widely used because of its important features such as resistant to criminal tampering, high accuracy, ease of feature extraction and greater authentication speed. The feature vein patterns extracted by Contourlet transform decompose into directional sub bands in different orientations at various scales. The Support Vector Machine (SVM) classifier is used for pattern matching. Thus the experimental results shows that our proposed method tested on two different databases of finger vein images improves recognition rate with high matching speed.

scholarly journals Vowel recognition of patients after total laryngectomy using Mel Frequency Cepstral Coefficients and mouth contour

2010 ◽  
Vol 20 (1) ◽  
pp. 33-38 ◽  
Author(s):  
Rafal Pietruch ◽  
Antoni Grzanka
Keyword(s):  
Support Vector Machines ◽  
Total Laryngectomy ◽  
Vocal Tract ◽  
Recognition Rate ◽  
Video Signal ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Mel Frequency Cepstral Coefficients ◽  
Vector Machines ◽  
Cepstral Coefficients

The paper addresses a problem of isolated vowels recognition in patients following total laryngectomy. The visual and acoustic speech modalities were separately incorporated in the machine learning algorithms. The authors used the Mel Frequency Cepstral Coefficients as acoustic descriptors of a speech signal. A lip contour was extracted from a video signal of the speaking faces using OpenCV software library. In a vowels recognition procedure the three types of classifiers were used for comparison purposes: Artificial Neural Networks, Support Vector Machines and Naive Bayes. The highest recognition rate was evaluated using Support Vector Machines. For a group of the laryngectomees having a different quality of speech the authors achieved 75% for acoustic and 40% for visual recognition performances. The authors obtained higher recognition rate than in a previous research where 10 cross-sectional areas of a vocal tract were estimated. Using presented image processing algorithm the visual features can be extracted automatically from a video signal.

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