scholarly journals Perbandingan Ekstraksi Ciri Full, Blocks, dan Row Mean Spectrogram Image Dalam Mengidentifikasi Pembicara

2014 ◽  
Vol 10 (1) ◽  
pp. 155
Author(s):  
La Ode Hasnuddin Sagala ◽  
Agus Harjoko
Keyword(s):  
Feature Extraction ◽  
Feature Vector ◽  
Speaker Identification ◽  
Extraction Methods ◽  
Image Feature ◽  
Sound Recording ◽  
Feature Size ◽  
Feature Extraction Method ◽  
Identification Rate ◽  
Optimal Feature

AbstrakPada sebuah sistem recognition, pemilihan metode ekstraksi ciri dan ukuran fitur yang digunakan mempengaruhi tingkat keakuratan identifikasi. Berkaitan dengan hal itu, dalam penelitian ini akan dijabarkan perbandingan tiga metode ekstraksi ciri CBIR yaitu row mean image, full image, dan blocks image. Ketiga metode tersebut digunakan untuk mengidentifikasi pembicara dengan menitikberatkan pada ukuran selection feature vector yang digunakan.Data suara diperoleh dari rekaman suara menggunakan handphone. Rekaman suara berasal dari 10 orang narasumber dengan rincian 5 pria dan 5 wanita. Setiap narasumber mengucapkan lima buah kalimat yaitu Selamat Pagi, Selamat Siang, Selamat Sore, Selamat Malam, dan Dengan Siapa serta diulangi delapan kali tiap kalimat.Karena menerapkan metode CBIR maka rekaman suara yang berbentuk sinyal dikonversi menjadi image spectrogram menggunakan STFT. Kemudian spectrogram diimplementasikan ke kekre transform lalu diekstrasi cirinya. Penggunaan kekre transform bertujuan untuk menyeleksi dan mengambil kemungkinan-kemungkinan fitur yang optimal serta juga meringankan proses komputasi.Menggunakan data reference 250 image spectrogram dan data testing 150 image spectrogram memberikan hasil bahwa metode ekstraksi ciri full image memperoleh persentase identifikasi lebih tinggi yaitu 93,3% dengan ukuran fitur 32x32. Kata kunci— Identifikasi pembicara, Spektrogram, Transformasi kekre, Full image, Blocks Image, Row mean image AbstractOn a system of recognition, selection feature extraction method and feature size are used in identification affects identication rate. In that regard, this study will presents comparison three feature extraction methods namely row mean image, full image, and blocks image. The third method used to identify the speaker with a focus on the size selection feature vector are used. Sound data obtained from the mobile phone voice recording. Sound recording derived from 10 speakers consisting of 5 men and 5 women. Every speakers pronounce five sentences are Selamat Pagi, Selamat Siang, Selamat Sore, Selamat Malam, and Dengan siapa as well as repeated eight times.Because applying CBIR methods then the sound recording signal is converted into an image spectrogram using STFT. Spectrogram is formed implemented in kekre transform to extract feature. Using kekre transform aims to select and take the possibilities optimal feature also relieves the computing process.Using reference data 250 spectrogram and testing data 150 spectrogram produces results that the full image feature extraction methods obtain a higher percentage identification rate is 93,3% with a feature size of 32x32. Keywords— Speaker identification, Spectrogram, Kekre Transform, Full Image, Blocks Image, Row Mean Image

Research on Image Feature Extraction Method Based on Orthogonal Projection Transformation of Multi-Task Learning Technology

2013 ◽  
Vol 760-762 ◽  
pp. 1609-1614
Author(s):  
Xiao Yuan Jing ◽  
Li Li ◽  
Cai Ling Wang ◽  
Yong Fang Yao ◽  
Feng Nan Yu
Keyword(s):  
Feature Extraction ◽  
Extraction Methods ◽  
Classification Performance ◽  
Image Feature ◽  
Learning Technology ◽  
Feature Extraction Method ◽  
Task Learning ◽  
Related Information ◽  
Training Samples ◽  
Projection Transformation

When the number of labeled training samples is very small, the sample information we can use would be very little. Because of this, the recognition rates of some traditional image recognition methods are not satisfactory. In order to use some related information that always exist in other databases, which is helpful to feature extraction and can improve the recognition rates, we apply multi-task learning to feature extraction of images. Our researches are based on transferring the projection transformation. Our experiments results on the public AR, FERET and CAS-PEAL databases demonstrate that the proposed approaches are more effective than the general related feature extraction methods in classification performance.

Fault diagnosis of rotating machinery based on time-frequency image feature extraction

2020 ◽  
Vol 39 (4) ◽  
pp. 5193-5200
Author(s):  
Shiyi Zhang ◽  
Laigang Zhang ◽  
Teng Zhao ◽  
Mahmoud Mohamed Selim
Keyword(s):  
Feature Extraction ◽  
Local Binary Pattern ◽  
Texture Features ◽  
Extraction Methods ◽  
Image Feature ◽  
Simulation Data ◽  
Image Feature Extraction ◽  
Feature Extraction Method ◽  
Time Frequency ◽  
Better Than

Aiming at the characteristics of time-frequency analysis of unsteady vibration signals, this paper proposes a method based on time-frequency image feature extraction, which combines non-downsampling contour wave transform and local binary mode LBP (Local Binary Pattern) to extract the features of time-frequency image faults. SVM is used for classification and recognition. Finally, the method is verified by simulation data. The results show that the classification accuracy of the method reaches 98.33%, and the extracted texture features are relatively stable. Also, the method is compared with the other 3 feature extraction methods. The results also show that the classification effect of the method is better than that of the traditional feature extraction method.

New Feature Extraction Method Based on Contourlet Transform for Banknote Classification

2013 ◽  
Vol 321-324 ◽  
pp. 1061-1065
Author(s):  
Guo Wei Yang ◽  
Wen Ling Wang ◽  
Shan Gai
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Extraction Methods ◽  
Image Feature ◽  
Contourlet Transform ◽  
Image Texture ◽  
Statistical Characteristics ◽  
Image Feature Extraction ◽  
Feature Extraction Method ◽  
New Feature

In order to improve the performance of the banknote classification, new banknote image feature extraction method is proposed in this paper. The contourlet transform is applied to the original banknote image which is obtained by image contact sensor.The statistical characteristics of transformed image in the contourlet domain are analyzed. The statistical characteristics which can perfectly reflect the banknote image texture information are used as feature vector for banknote classification. The experimental results show that the proposed method can obtain higher recognition compared with other conventional banknote image feature extraction methods.

A Structural Analysis Based Feature Extraction Method for OCR System For Myanmar Printed Document Images

2012 ◽  
Vol 2 (1) ◽  
pp. 16-41 ◽  
Author(s):  
Htwe Pa Pa Win ◽  
Phyo Thu Thu Khine ◽  
Khin Nwe Ni Tun
Keyword(s):  
Feature Extraction ◽  
Structural Analysis ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Extraction Method ◽  
Recognition Performance ◽  
Extraction Methods ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Extraction Method

This paper proposes a new feature extraction method for off-line recognition of Myanmar printed documents. One of the most important factors to achieve high recognition performance in Optical Character Recognition (OCR) system is the selection of the feature extraction methods. Different types of existing OCR systems used various feature extraction methods because of the diversity of the scripts’ natures. One major contribution of the work in this paper is the design of logically rigorous coding based features. To show the effectiveness of the proposed method, this paper assumed the documents are successfully segmented into characters and extracted features from these isolated Myanmar characters. These features are extracted using structural analysis of the Myanmar scripts. The experimental results have been carried out using the Support Vector Machine (SVM) classifier and compare the pervious proposed feature extraction method.

Research on Feature Extraction Method Based on Rub-Impact Acoustic Emission Signals of Mechanical Seal End Faces

2010 ◽  
Vol 36 ◽  
pp. 68-74
Author(s):  
Chuan Jun Liao ◽  
Shuang Fu Suo ◽  
Wei Feng Huang
Keyword(s):  
Acoustic Emission ◽  
Feature Extraction ◽  
Extraction Methods ◽  
Mechanical Seal ◽  
Mechanical Seals ◽  
Feature Extraction Method ◽  
Different Types ◽  
Ae Signal ◽  
Feature Information ◽  
Ae Signals

Acoustic emission (AE) techniques are put forward to monitor rub-impacts between rotating rings and stationary rings of mechanical seals by this paper. By analyzing feature extraction methods of the typical rub-impact AE signal, the method combining of wavelet scalogram and power spectrum is found useful, and can used to attribute the feature information implicated in rub-impact AE signals of mechanical seal end faces. Both simulations and experimental research prove that the method is effective, and are used successfully to identify the typical features of different types of rub-impacts of mechanical seal end faces.

scholarly journals PERBANDINGAN IDENTIFIKASI PEMBICARA MENGGUNAKAN MFCC DAN SBC DENGAN CIRI PENCOCOKAN LBG-VQ

2018 ◽  
Author(s):  
Purwono Prasetyawan
Keyword(s):  
Feature Extraction ◽  
Human Behavior ◽  
Vector Quantization ◽  
Speaker Identification ◽  
Extraction Methods ◽  
Behavioral Characteristics ◽  
Physical Analysis ◽  
Test Results ◽  
Voice Signal ◽  
Signal Characteristics

Biometrics is a technology for physical analysis and human behavior used in authentication. One of the behavioral characteristics associated with a person is sound. A person's voice can be identified based on the person's voice signal characteristics. There are several methods in recognizing speaker sound, such as with Mel Frequency Cepstrum Calculation (MFCC) and Subband Based Cepstral (SBC). This study looked for the effectiveness of the use of MFCC and SBC feature extraction with LBG Vector Quantization matching characteristics. Effective feature extraction methods will be tested for realtime speaker identification. The results obtained from this research is the value of MFCC 32 coefficient more effective than the SBC accurately and the speed of the process of identification of both text-dependent and text-independent speaker. The test results of speaker identification in realtime using MFCC is still not satisfactory because the accuracy of recognition is still below 70%.

scholarly journals A REVIEW OF FEATURE EXTRACTION METHODS ON MACHINE LEARNING

2021 ◽  
Vol 6 (22) ◽  
pp. 51-59
Author(s):  
Mustazzihim Suhaidi ◽  
Rabiah Abdul Kadir ◽  
Sabrina Tiun
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Feature Selection ◽  
Input Data ◽  
Feature Vector ◽  
Learning Algorithm ◽  
Extraction Methods ◽  
Machine Learning Algorithm ◽  
Learning Tasks ◽  
Low Dimensional

Extracting features from input data is vital for successful classification and machine learning tasks. Classification is the process of declaring an object into one of the predefined categories. Many different feature selection and feature extraction methods exist, and they are being widely used. Feature extraction, obviously, is a transformation of large input data into a low dimensional feature vector, which is an input to classification or a machine learning algorithm. The task of feature extraction has major challenges, which will be discussed in this paper. The challenge is to learn and extract knowledge from text datasets to make correct decisions. The objective of this paper is to give an overview of methods used in feature extraction for various applications, with a dataset containing a collection of texts taken from social media.

scholarly journals Unsupervised Text Feature Extraction for Academic Chatbot using Constrained FP-Growth

2021 ◽  
Vol 14 ◽  
pp. 1-11
Author(s):  
Suraya Alias
Keyword(s):  
Feature Extraction ◽  
Customer Service ◽  
User Satisfaction ◽  
Feature Vector ◽  
Size Reduction ◽  
Growth Technique ◽  
Feature Extraction Method ◽  
Text Feature ◽  
Text Features ◽  
N Gram

In the edge where conversation merely involves online chatting and texting one another, an automated conversational agent is needed to support certain repetitive tasks such as providing FAQs, customer service and product recommendations. One of the key challenges is to identify and discover user’s intention in a social conversation where the focus of our work in the academic domain. Our unsupervised text feature extraction method for Intent Pattern Discovery is developed by applying text features constraints to the FP-Growth technique. The academic corpus was developed using a chat messages dataset where the conversation between students and academicians regarding undergraduate and postgraduate queries were extracted as text features for our model. We experimented with our new Constrained Frequent Intent Pattern (cFIP) model in contrast with the N-gram model in terms of feature-vector size reduction, descriptive intent discovery, and analysis of cFIP Rules. Our findings show significant and descriptive intent patterns was discovered with confidence rules value of 0.9 for cFIP of 3-sequence. We report an average feature-vector size reduction of 76% compared to the Bigram model using both undergraduate and postgraduate conversation datasets. The usability testing results depicted overall user satisfaction average mean score is 4.30 out of 5 in using the Academic chatbot which supported our intent discovery cFIP approach.

Lung Disease Classification by Novel Shape-Based Feature Extraction and New Hybrid Genetic Approach

2016 ◽  
pp. 321-346
Author(s):  
Bhuvaneswari Chandran ◽  
P. Aruna ◽  
D. Loganathan
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Lung Diseases ◽  
Hybrid Genetic Algorithm ◽  
Extraction Methods ◽  
Disease Classification ◽  
Filter Method ◽  
Svm Classifier ◽  
Genetic Approach ◽  
Feature Extraction Method

The purpose of the chapter is to present a novel method to classify lung diseases from the computed tomography images which assist physicians in the diagnosis of lung diseases. The method is based on a new approach which combines a proposed M2 feature extraction method and a novel hybrid genetic approach with different types of classifiers. The feature extraction methods performed in this work are moment invariants, proposed multiscale filter method and proposed M2 feature extraction method. The essential features which are the results of the feature extraction technique are selected by the novel hybrid genetic algorithm feature selection algorithms. Classification is performed by the support vector machine, multilayer perceptron neural network and Bayes Net classifiers. The result obtained proves that the proposed technique is an efficient and robust method. The performance of the proposed M2 feature extraction with proposed hybrid GA and SVM classifier combination achieves maximum classification accuracy.

scholarly journals Vibration Feature Analysis for Gas-Insulated Switchgear Mechanical Fault Detection under Varying Current

2020 ◽  
Vol 10 (3) ◽  
pp. 944 ◽  
Author(s):  
Ying Feng ◽  
Jianwen Wu
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Extraction Methods ◽  
Mechanical Analysis ◽  
Linear Vibration ◽  
Feature Extraction Method ◽  
Gas Insulated Switchgear ◽  
Non Linear ◽  
Actual Operation ◽  
Mechanical Defect

As a key component to ensure the safe operation of the power grid, mechanical defect diagnosis technology of gas-insulated switchgear (GIS) during operation is often neglected. At present, GIS mechanical fault detection based on vibration information has not been developed. The main reason is that the excitation current is considerable but uncontrollable in the actual operation of GIS. It is difficult to eliminate the influence of excitation on the vibration amplitude and form an effective vibration feature description technology. Therefore, this paper proposes a unified feature-extraction method for GIS vibration information that reduces the influence of current amplitude for mechanical fault diagnosis. Starting from the GIS mechanical analysis, the periodicity of vibration excitation and the influence of amplitude are discussed. Then, combined with the non-linear characteristics of GIS systems and non-linear vibration theory, the multiplier frequency energy ratio (MFER) is designed to extract vibration-unified features of GIS for diagnosing the mechanical fault under different current levels. The diagnosis results of the experimental data with different feature-extraction methods show the applicability and superiority of the proposed method in the GIS’s mechanical fault-detection field based on vibration information.

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