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Articles 1 - 30 of 49
Full-Text Articles in Engineering
Secured Blockchain And Fractional Discrete Cosine Transform-Based Framework For Medical Images, Abhay Kumar Yadav, Virendra P. Vishwakarma
Secured Blockchain And Fractional Discrete Cosine Transform-Based Framework For Medical Images, Abhay Kumar Yadav, Virendra P. Vishwakarma
Makara Journal of Technology
Images can store large amounts of data and are useful for transmitting large amounts of information across different geographical locations using different cloud services. This data sharing increases the chances of cyber-attacks on digital images. Blockchain has properties that enable it to work as a solution to this problem, providing enhanced security and unchangeable storage. However, image size poses a challenge in image storage, as it increases the related storage cost. Compressing images using fractional discrete cosine transform (fctDCT) reduces the amount of data required to express an image securely. This paper presents a novel framework for securely storing and …
3d Streamline Visualization Method Based On Clustering Fusion, Xuqiang Shao, Ya Cheng, Yizhong Jin
3d Streamline Visualization Method Based On Clustering Fusion, Xuqiang Shao, Ya Cheng, Yizhong Jin
Journal of System Simulation
Abstract: In order to solve the problems of incomplete feature extraction, continuity destruction of flow field by visual results, and poor representation of streamline caused by unstable clustering division when the clustering method is used to realize 3D streamline visualization. A 3D streamline visualization method based on clustering fusion is proposed. It consists of a distance measurement method between features and a clustering fusion method, which takes the inter-feature distance and spatial distance as the similarity between streamlines for clustering and then performs weighted merging and subdivision of the obtained clustering result. The method has been tested on data sets …
A Practical Low-Dimensional Feature Vector Generation Method Based On Wavelet Transform For Psychophysiological Signals, Erdem Erkan, Yasemi̇n Erkan
A Practical Low-Dimensional Feature Vector Generation Method Based On Wavelet Transform For Psychophysiological Signals, Erdem Erkan, Yasemi̇n Erkan
Turkish Journal of Electrical Engineering and Computer Sciences
High-dimensional feature vectors entail computational cost and computational complexity. However, a successful classification can be obtained with an optimally sized feature vector consisting of distinctive features. With the widespread use of the internet and mobile devices, the need for systems with low computational costs is increasing day by day. In this study, starting from the idea that each motor imagery is represented as a subject-specific pattern in the brain, we propose a new and practical method that can generate a low-dimensional feature vector based on wavelet transform. The feature vector is obtained from the correlation between each trial and each …
Research On Aquifer Water Abundance Evaluation By Borehole Transient Electromagnetic Method Based On Fcnn, Cheng Jiulong, Wang Huijie, Xu Zhongzhong, Huang Qisong, Jiang Guoqing
Research On Aquifer Water Abundance Evaluation By Borehole Transient Electromagnetic Method Based On Fcnn, Cheng Jiulong, Wang Huijie, Xu Zhongzhong, Huang Qisong, Jiang Guoqing
Coal Geology & Exploration
Predicting the location and water abundance of aquifers by drilling or geophysical methods before tunneling is very important to prevent water disasters in advance for the safety of coal mine production. The use of borehole transient electromagnetic method (BTEM) for advanced detection has obvious advantages. At present, the interpretation method is based on the calculated resistivity to qualitatively analyze rock water abundance, and it is impossible to predict the aquifer water abundance grade. So, the fully convolutional neural network (FCNN) method is proposed to predict the aquifer water abundance grade for BTEM. Firstly, according to the Archie formula, Kozeny-Carman formula, …
An Exploratory Study On The Effect Of Applying Various Artificial Neural Networks To The Classification Of Lower Limb Injury, Rachel Yun, May Salama, Lamiaa Elrefaei
An Exploratory Study On The Effect Of Applying Various Artificial Neural Networks To The Classification Of Lower Limb Injury, Rachel Yun, May Salama, Lamiaa Elrefaei
Turkish Journal of Electrical Engineering and Computer Sciences
This paper explores the application of a deep neural network (DNN) framework to human gait analysis for injury classification. The paper aims to identify whether a subject is healthy or has an injury of the ankle, knee, hip, or heel solely based on ground reaction force plate measurements. We consider how three DNNs-the multi-layer perceptron (MLP), fully convolutional network (FCN), and residual network (ResNet)-can be applied to gait analysis when the number of trainable network parameters far exceeds the number of training samples, and benchmark their performance in this context against that of shallow neural networks. The DNN architectures outperformed …
Uzbek Commands Recognition By Processing The Spectrogram Image, M M. Musayev, I Sh Khujayorov, M I. Abdullaeva, M M. Ochilov
Uzbek Commands Recognition By Processing The Spectrogram Image, M M. Musayev, I Sh Khujayorov, M I. Abdullaeva, M M. Ochilov
Technical science and innovation
This paper describes the most common algorithms with image approach convolutional neural network and two-dimensional DCT with machine learning classification KNN, SVM and RF. These algorithms are evaluated for applicability to the Uzbek language and a comparative analysis on the accuracy and recognition rate. The command words of the Uzbek language were chosen for the experiments. According to the results, it was found that both methods give high rates of recognition accuracy and are 92% (CNN) and 90% (2DDCT+Zigzag+SVM). Also the combinations of 2D-DCT+Zigzag+ KNN and 2D-DCT+Zigzag+ RF with average recognition accuracy of 86% and 85%, respectively, were considered in …
A Comprehensive Survey For Non-Intrusive Load Monitoring, Efe İsa Tezde, Eray Yildiz
A Comprehensive Survey For Non-Intrusive Load Monitoring, Efe İsa Tezde, Eray Yildiz
Turkish Journal of Electrical Engineering and Computer Sciences
Energy-saving and efficiency are as important as benefiting from new energy sources to supply increasing energy demand globally. Energy demand and resources for energy saving should be managed effectively. Therefore, electrical loads need to be monitored and controlled. Demand-side energy management plays a vital role in achieving this objective. Energy management systems schedule an optimal operation program for these loads by obtaining more accurate and precise residential and commercial loads information. Different intellegent measurement applications and machine learning algorithms have been proposed for the measurement and control of electrical devices/loads used in buildings. Of these, nonintrusive load monitoring (NILM) is …
Engine Wear Fault Diagnosis Based On Supervised Kernel Entropy Component Analysis, Zhichao Zhu, Dinghui Wu, Yuanchang Yue
Engine Wear Fault Diagnosis Based On Supervised Kernel Entropy Component Analysis, Zhichao Zhu, Dinghui Wu, Yuanchang Yue
Journal of System Simulation
Abstract: Focus on the influence of environment on engine operation, which leads to a large amount of redundant information and nonlinear structure in oil spectral data that affects the engine fault diagnosis results, the feature extraction method of SKECA (supervised kernel entropy component analysis) is proposed. A supervised learning algorithm is adopted on the basis of Kernel Entropy Component Analysis, which extracts the inherent geometric features of oil spectrum data to make the extracted fault features include the discriminative information. GA (genetic algorithm) is used to find parameters to optimize the results of feature extraction, and SVM (support vector machine) …
Automated Classification Of Bi-Rads In Textual Mammography Reports, Mostafa Boroumandzadeh, Elham Parvinnia
Automated Classification Of Bi-Rads In Textual Mammography Reports, Mostafa Boroumandzadeh, Elham Parvinnia
Turkish Journal of Electrical Engineering and Computer Sciences
The main purpose of this paper is to process key information in medical text records and also classifypatients, per different levels of breast imaging-reporting and data system (BI-RADS). The BI-RADS is a scheme for thestandardization of breast imaging reports. Therefore, medical text mining is employed to classify mammography reportssupported BI-RADS. In this research, a new method is proposed for automated BI-RADS classifications extraction fromtextual reports and improves the therapeutic procedures. At first, a mammography lexicon is employed for choosingkeywords from medical text reports. Word2vec and term frequency inverse document frequency (TFIDF) techniques areused for extracting features, finally, they are combined …
Performance Evaluation Of Hht And Wt For Detection Of Hif And Ct Saturationin Smart Grids, Saeid Heidari, Saeed Asgharigovar, Pouya Pourghasem, Heresh Seyedi, Ömer Usta
Performance Evaluation Of Hht And Wt For Detection Of Hif And Ct Saturationin Smart Grids, Saeid Heidari, Saeed Asgharigovar, Pouya Pourghasem, Heresh Seyedi, Ömer Usta
Turkish Journal of Electrical Engineering and Computer Sciences
Hilbert-Huang transform (HHT), continuous wavelet transform (CWT) and discrete wavelet transform (DWT) are well-known signal processing methods that are widely utilized for feature extraction and fault detection by protection systems in smart grids. In this paper, we assess the performances of these methods encountering challenging situations in distribution networks, i.e. high impedance arcing fault (HIF) and current transformer (CT) saturation. Low fault current amplitude in HIF case causes the overcurrent protection, which is the predominant protection method in distribution grids, to fail. Furthermore, some faults may lead to CT saturation, which may result in delayed operation of the relay. To …
Swft: Subbands Wavelet For Local Features Transform Descriptor For Cornealdiseases Diagnosis, Samer Al-Salihi, Sezgi̇n Aydin, Nebras Hussein
Swft: Subbands Wavelet For Local Features Transform Descriptor For Cornealdiseases Diagnosis, Samer Al-Salihi, Sezgi̇n Aydin, Nebras Hussein
Turkish Journal of Electrical Engineering and Computer Sciences
Human cornea is the front see-through shield of the eye. It refracts light onto the retina to induce vision.Therefore, any defect in the cornea may lead to vision disturbance. This deficiency is estimated by sets of topographicalimages measured, and assessed by an ophthalmologist. Consequently, an important priority is the early and accuratediagnosis of diseases that may affect corneal integrity through the use of machine learning algorithms. Images producedby a Pentacam device can be subjected to rotation or some distortion during acquisition; therefore, accurate diagnosisrequires the use of local features in the image. Accordingly, a new algorithm called subbands wavelet for …
A Novel Pulse Plethysmograph Signal Analysis Method For Identification Of Myocardial Infarction, Dilated Cardiomyopathy, And Hypertension, Muhammad Umar Khan, Sumair Aziz
A Novel Pulse Plethysmograph Signal Analysis Method For Identification Of Myocardial Infarction, Dilated Cardiomyopathy, And Hypertension, Muhammad Umar Khan, Sumair Aziz
Turkish Journal of Electrical Engineering and Computer Sciences
Cardiac diseases (CDs) are one of the leading causes of the growing global mortality rate. Early detectionof CDs is necessary to avoid a high increase in the mortality rate. Machine learning-based computer-aided diagnosisof CDs using various physiological signals has recently been used by researchers. Since pulse plethysmograph (PuPG)signal contains a wealth of information about cardiac pathologies, therefore, this paper presents an expert system designfor the automatic diagnosis of cardiac disorders like hypertension, dilated cardiomyopathy and myocardial infarctionusing a novel fingertip PuPG signal analysis. The proposed system first performs signal denoising of raw PuPG sensordata using discrete wavelet transform (DWT). After …
Study On Hand Gesture Recognition And Portfolio Optimization Model Based On Svm, Zhiwei Cai, Shuyan Wu, Junfeng Song
Study On Hand Gesture Recognition And Portfolio Optimization Model Based On Svm, Zhiwei Cai, Shuyan Wu, Junfeng Song
Journal of System Simulation
Abstract: Hand gesture recognition was researched. The idea of extracting related features was proposed by using SVM algorithm in machine learning domain, and combination optimization method was used, which consists of ANN, HMM and DTW, to do hand gesture recognition. The experimental results show that portfolio optimization model based gesture recognition method has high accuracy and is very effective.
Heat Kernel Signature Extraction Algorithm Based On Mesh Simplification, Haisheng Li, Sun Li, Cai Qiang, Cao Jian
Heat Kernel Signature Extraction Algorithm Based On Mesh Simplification, Haisheng Li, Sun Li, Cai Qiang, Cao Jian
Journal of System Simulation
Abstract: Heat kernel signature has been proposed for 3D model feature extraction in recent years. However, the performance of heat kernel signature is inefficient, especially when the models have large number of vertices. Mesh simplification algorithm based on quadric error metrics was used to preprocess 3D model and the heat kernel signature was calculated based on the simplified model. Experiments show that the feature extracting time of the simplified model is less than the original model. The more vertices of the original model, the more obvious of the improved efficiency. The heat kernel signature of simplified model is consistent with …
Improved Method Of Extracting Hks Descriptors And Non-Rigid Classification Applications, Jingyu Jiang, Lili Wan
Improved Method Of Extracting Hks Descriptors And Non-Rigid Classification Applications, Jingyu Jiang, Lili Wan
Journal of System Simulation
Abstract: In order to make the HKS（heat kernel signature）have wider applicability in non-rigid shape analysis, an improved method of extracting HKS descriptors for unconnected non-rigid 3D models was proposed. The largest connected component was obtained. The HKS descriptors of the largest connected component were calculated and those descriptors of the boundary vertices and their 1-ring neighbors were excluded. For shape classifications, the dictionary was learned for each class based on the sparse representation theory. For a test model, each dictionary was utilized to sparsely represent its descriptor set, and the most appropriate dictionary was determined by the representation error, …
Research Of Merging Three-Dimensional Static Scene And Moving Objects Based On Video, Kunjin He, Wang Lin, Jianxin Liu, Zhengming Chen, Xiaozhong Chen
Research Of Merging Three-Dimensional Static Scene And Moving Objects Based On Video, Kunjin He, Wang Lin, Jianxin Liu, Zhengming Chen, Xiaozhong Chen
Journal of System Simulation
Abstract: Against the difficulty in three-dimensional real-time rendering of scene that contains moving objects, a video-based method in building and merging three-dimensional static scene and moving objects was proposed. According to the classification of moving objects, a three-dimensional static scene was established and a parameterized model base was produced. Based on the video, moving objects were detected and features (including basic features and dynamic features) were extracted. Based on basic features, corresponding type model in the parameterized model base was instantiated. Based on the dynamic features, the three-dimensional static scene and moving object models were merged and the …
Research On Simulation Of Multi-Target Micro-Doppler Separation And Extraction In Ballistic Midcourse, Yizhe Wang, Cunqian Feng, Jingqing Li
Research On Simulation Of Multi-Target Micro-Doppler Separation And Extraction In Ballistic Midcourse, Yizhe Wang, Cunqian Feng, Jingqing Li
Journal of System Simulation
Abstract: Aiming at the intricate overlap and difficult separation and extraction of micro-Doppler information in Doppler spectra of warheads and fragments in midcourse, a novel method based on CEEMD and improved self-adaptive Viterbi algorithm was proposed. By analyzing the differences of micro-Doppler distribution between warheads and fragments, the echo was decomposed by CEEMD and each IMF was denoised by wavelet threshold denoising method, resulting in separation of warheads and fragments echo. The fragments signal was stretched, the optimal path was extracted combined with improved self-adaptive Viterbi algorithm, and the separation of multi-target signal and extraction of micro-Doppler was realized. Simulation …
Motion Object Feature Extraction Method Based On Multi-Feature Fusion, Xidao Luan, Yuxiang Xie, Zhang Xin, Niu Xiao
Motion Object Feature Extraction Method Based On Multi-Feature Fusion, Xidao Luan, Yuxiang Xie, Zhang Xin, Niu Xiao
Journal of System Simulation
Abstract: Motion object feature extraction is the basis of motion object classification. Traditionally motion object classification mainly depends on single feature extraction which is sensitive to the aspects like motion object detection area, angle, scale and noise disturbance, thus decreases the classification efficiency. To solve these problems and improve the robustness of the algorithms, a motion object feature extraction method based on multi-feature fusion was proposed. In this method, width height ratio feature, rotation invariant uniform local binary pattern feature and SIFT feature were considered, and by fusing them into the SVM and KNN classifier, motion object classification was carried …
Multi-Pose Pedestrian Detection Based On Posterior Multiple Sparse Dictionaries, Lingkang Gu, Mingzheng Zhou, Wang Jun, Xiu Yu
Multi-Pose Pedestrian Detection Based On Posterior Multiple Sparse Dictionaries, Lingkang Gu, Mingzheng Zhou, Wang Jun, Xiu Yu
Journal of System Simulation
Abstract: In order to detect pedestrians effectively, a multi-pose pedestrian detection method based on posterior multiple sparse dictionaries was proposed. Through pre-learning multiple different sparse dictionaries, and sparse coding the image, statistics for each dictionary corresponds to sparse coding histogram as the pedestrian image feature descriptor. The common information of multiple sparse dictionary features of all positive samples was obtained, and the feature of a single pedestrian sample was weighted, and the features of a posteriori multiple sparse dictionary could be obtained. Then pedestrians of different poses and views were divided into subclasses with clustering algorithm. A classifier was trained …
Retinal Vessel Segmentation Using Modified Symmetrical Local Threshold, Umar Özgünalp
Retinal Vessel Segmentation Using Modified Symmetrical Local Threshold, Umar Özgünalp
Turkish Journal of Electrical Engineering and Computer Sciences
Retinal vessel segmentation is important for the identification of many diseases including glaucoma, hypertensive retinopathy, diabetes, and hypertension. Moreover, retinal vessel diameter is associated with cardiovascular mortality. Accurate detection of blood vessels improves the detection of exudates in color fundus images, as well as detection of the retinal nerve, optic disc, or fovea. A retinal vessel is a darker stripe on a lighter background. Thus, the objective is very similar to the lane detection task for intelligent vehicles. A lane on a road is a light stripe on a darker background (i.e. asphalt). For lane detection, the symmetrical local threshold …
Short Unsegmented Pcg Classification Based On Ensemble Classifier, Sinam Ajitkumar Singh, Swanirbhar Majumder
Short Unsegmented Pcg Classification Based On Ensemble Classifier, Sinam Ajitkumar Singh, Swanirbhar Majumder
Turkish Journal of Electrical Engineering and Computer Sciences
Diseases associated with the heart are one of the main reasons of death worldwide. Hence, early examination of the heart is important. For analysis of cardiac disorders, a study of heart sounds is a crucial and beneficial approach. Still, automated classification of heart sounds is a challenging task that mainly depends on segmentation of heart sounds and derivation of features using segmented samples. In the literature available for PCG classification provided by PhysioNet/CinC Challenge 2016, most of the research has focused on enhancing the accuracy of the classification model based on complicated segmentation processes and has failed to improve the …
A Detailed Survey Of Turkish Automatic Speech Recognition, Recep Si̇nan Arslan, Necaatti̇n Barişçi
A Detailed Survey Of Turkish Automatic Speech Recognition, Recep Si̇nan Arslan, Necaatti̇n Barişçi
Turkish Journal of Electrical Engineering and Computer Sciences
Significant improvements have been made in automatic speech recognition(ASR)systems in terms of both the general technology and the software used. Despite these advancements, however, there is still an important difference between the recognition performance of humans and machines. This work focuses on the studies conducted in the field of Turkish speech recognition, the progress made in such studies in recent years, the language-specific constraints, the performance results achieved in the applications developed to date, and the development of a general scheme for researchers wishing to develop an ASR system for the Turkish language. A comprehensive study on the Turkish language, …
Research On Augmented Reality Method Based On Unmarked Recognition, Li Qian, Shangbing Gao, Zhigeng Pan, Zhengwei Zhang, Chenghua Fang, Shengquan Wang
Research On Augmented Reality Method Based On Unmarked Recognition, Li Qian, Shangbing Gao, Zhigeng Pan, Zhengwei Zhang, Chenghua Fang, Shengquan Wang
Journal of System Simulation
Abstract: As a kind of technology of superposing the virtual objects upon reality scene, AR (Augment Reality) expands the information quantity of the actual scene by virtual information. Aiming at the deficiency of previous AR application methods, the AR application method of unmarked recognition based on the optimized filtering was proposed in this paper. In this algorithm, filtering was used for image preprocessing to solve the poor matching efficiency problems of the past recognition algorithms, then three-dimensional model was established in two-dimensional images and the model was rendered in the two-dimensional plane. The experimental results show that, compared with …
Application Of Virtual Trial Makeup Based On Video, Jiayuan Liu, Jinfang Li, Hanwu He
Application Of Virtual Trial Makeup Based On Video, Jiayuan Liu, Jinfang Li, Hanwu He
Journal of System Simulation
Abstract: To try different cosmetic products in a convenient and low cost way, a virtual make-up algorithm based on a plane mesh model is proposed. The feature points are extracted from the face in the video by the Harr cascade classifier and the Dlib library. A mask pattern is built on the different parts of the face dynamically according to the texture coordinates of the plane mesh model. Theory is used to control the display area of the main texture of the planar mesh model. The mapping relationship between the main texture and the feature point coordinates of the planar …
A New Spectral Estimation-Based Feature Extraction Method For Vehicle Classification In Distributed Sensor Networks, Erdem Köse, Ali̇ Köksal Hocaoğlu
A New Spectral Estimation-Based Feature Extraction Method For Vehicle Classification In Distributed Sensor Networks, Erdem Köse, Ali̇ Köksal Hocaoğlu
Turkish Journal of Electrical Engineering and Computer Sciences
Ground vehicle detection and classification with distributed sensor networks is of growing interest for border security. Different sensing modalities including electro-optical, seismic, and acoustic were evaluated individually and in combination to develop a more efficient system. Despite previous works that mostly studied frequency-domain features and acoustic sensors, in this work we analyzed the classification performance for both frequency and time-domain features and seismic and acoustic modalities. Despite their infrequent use, we show that when fused with frequency-domain features, time-domain features improve the classification performance and reduce the false positive rate, especially for seismic signals. We investigated the performance of seismic …
Plant Disease And Pest Detection Using Deep Learning-Based Features, Muammer Türkoğlu, Davut Hanbay
Plant Disease And Pest Detection Using Deep Learning-Based Features, Muammer Türkoğlu, Davut Hanbay
Turkish Journal of Electrical Engineering and Computer Sciences
The timely and accurate diagnosis of plant diseases plays an important role in preventing the loss of productivity and loss or reduced quantity of agricultural products. In order to solve such problems, methods based on machine learning can be used. In recent years, deep learning, which is especially widely used in image processing, offers many new applications related to precision agriculture. In this study, we evaluated the performance results using different approaches of nine powerful architectures of deep neural networks for plant disease detection. Transfer learning and deep feature extraction methods are used, which adapt these deep learning models to …
Classification Of The Likelihood Of Colon Cancer With Machine Learning Techniques Using Ftir Signals Obtained From Plasma, Suat Toraman, Mustafa Gi̇rgi̇n, Bi̇lal Üstündağ, İbrahi̇m Türkoğlu
Classification Of The Likelihood Of Colon Cancer With Machine Learning Techniques Using Ftir Signals Obtained From Plasma, Suat Toraman, Mustafa Gi̇rgi̇n, Bi̇lal Üstündağ, İbrahi̇m Türkoğlu
Turkish Journal of Electrical Engineering and Computer Sciences
Colon cancer is one of the major causes of human mortality worldwide and the same can be said for Turkey. Various methods are used for the determination of cancer. One of these methods is Fourier transform infrared (FTIR) spectroscopy, which has the ability to reveal biochemical changes. The most common features used to distinguish patients with cancer and healthy subjects are peak densities, peak height ratios, and peak area ratios. The greatest challenge of studies conducted to distinguish cancer patients from healthy subjects using FTIR signals is that the signals of cancer patients and healthy subjects are similar. In the …
Biometric Person Authentication Framework Using Polynomial Curve Fitting-Based Ecg Feature Extraction, Şahi̇n Işik, Kemal Özkan, Semi̇h Ergi̇n
Biometric Person Authentication Framework Using Polynomial Curve Fitting-Based Ecg Feature Extraction, Şahi̇n Işik, Kemal Özkan, Semi̇h Ergi̇n
Turkish Journal of Electrical Engineering and Computer Sciences
The applications of modern biometric techniques for person identification systems rapidly increase for meeting the rising security demands. The distinctive physiological characteristics are more correctly measurable and trustworthy since previous measurements are not appropriately made for physiological properties. While a variety of strategies have been enabled for identification, the electrocardiogram (ECG)-based approaches are popular and reliable techniques in the senses of measurability, singularity, and universal awareness of heartbeat signals. This paper presents a new ECG-based feature extraction method for person identification using a huge amount of ECG recordings. First of all, 1800 heartbeats for each of the 36 subjects have …
Local Directional-Structural Pattern For Person-Independent Facial Expression Recognition, Farkhod Makhmudkhujaev, Md Tauhid Bin Iqbal, Byungyong Ryu, Oksam Chae
Local Directional-Structural Pattern For Person-Independent Facial Expression Recognition, Farkhod Makhmudkhujaev, Md Tauhid Bin Iqbal, Byungyong Ryu, Oksam Chae
Turkish Journal of Electrical Engineering and Computer Sciences
Existing popular descriptors for facial expression recognition often suffer from inconsistent feature description, experiencing poor accuracies. We present a new local descriptor, local directional-structural pattern (LDSP), in this work to address this issue. Unlike the existing local descriptors using only the texture or edge information to represent the local structure of a pixel, the proposed LDSP utilizes the positional relationship of the top edge responses of the target pixel to extract more detailed structural information of the local texture. We further exploit such information to characterize expression-affiliated crucial textures while discarding the random noisy patterns. Moreover, we introduce a globally …
Scale-Invariant Mfccs For Speech/Speaker Recognition, Zekeri̇ya Tüfekci̇, Gökay Di̇şken
Scale-Invariant Mfccs For Speech/Speaker Recognition, Zekeri̇ya Tüfekci̇, Gökay Di̇şken
Turkish Journal of Electrical Engineering and Computer Sciences
The feature extraction process is a fundamental part of speech processing. Mel frequency cepstral coefficients (MFCCs) are the most commonly used feature types in the speech/speaker recognition literature. However, the MFCC framework may face numerical issues or dynamic range problems, which decreases their performance. A practical solution to these problems is adding a constant to filter-bank magnitudes before log compression, thus violating the scale-invariant property. In this work, a magnitude normalization and a multiplication constant are introduced to make the MFCCs scale-invariant and to avoid dynamic range expansion of nonspeech frames. Speaker verification experiments are conducted to show the effectiveness …