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User Recognition System Based on spectrogram Image Conversion Using EMG Signals

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Computers, Materials & Continua 2022年第7期72卷 1213-1227页

作者： Jae Myung Kim Gyu Ho Choi Min-Gu Kim Sung Bum Pan Interdisciplinary Program in IT-Bio Convergence System Chosun UniversityGwangju61452Korea IT Research Institute Chosun UniversityGwangju61452Korea

Recently,user recognitionmethods to authenticate personal identity has attracted significant attention especially with increased availability of various internet of things(IoT)services through fifth-generation technology(5G)based mobile *** EMG signals generated inside the body with unique individual characteristics are being studied as a part of nextgeneration user recognition ***,there is a limitation when applying EMG signals to user recognition systems as the same operation needs to be repeated while maintaining a constant strength of muscle over ***,it is necessary to conduct research on multidimensional feature transformation that includes changes in frequency features over *** this paper,we propose a user recognition system that applies EMG signals to the short-time fourier transform(STFT),and converts the signals into EMG spectrogram images while adjusting the time-frequency resolution to extract multidimensional *** proposed system is composed of a data pre-processing and normalization process,spectrogram image conversion process,and final classification *** experimental results revealed that the proposed EMG spectrogram image-based user recognition system has a 95.4%accuracy performance,which is 13%higher than the EMGsignal-based *** a user recognition accuracy improvement was achieved by using multidimensional features,in the time-frequency domain.

关键词： EMG user recognition spectrogram CNN

Automatic Speaker Recognition Using Mel-Frequency Cepstral Coefficients Through Machine Learning

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Computers, Materials & Continua 2022年第6期71卷 5511-5521页

作者： U˘gur Ayvaz Hüseyin Gürüler Faheem Khan Naveed Ahmed Taegkeun Whangbo Abdusalomov Akmalbek Bobomirzaevich Department of Computer Engineering Istanbul Technical UniversityIstanbul34485Turkey Department of Information Systems Engineering Mugla Sitki Kocman UniversityMugla48000Turkey Artificial Intelligence Lab Department of Computer EngineeringGachon UniversitySeongnam13557Korea Department of Computer Science College of Computing and InformaticsUniversity of SharjahSharjah27272UAE

Automatic speaker recognition(ASR)systems are the field of Human-machine interaction and scientists have been using feature extraction and feature matching methods to analyze and synthesize these *** of the most commonly used methods for feature extraction is Mel Frequency Cepstral Coefficients(MFCCs).Recent researches show that MFCCs are successful in processing the voice signal with high *** represents a sequence of voice signal-specific *** experimental analysis is proposed to distinguish Turkish speakers by extracting the MFCCs from the speech *** the human perception of sound is not linear,after the filterbank step in theMFCC method,we converted the obtained log filterbanks into decibel(dB)features-based spectrograms without applying the Discrete Cosine Transform(DCT).A new dataset was created with converted spectrogram into a 2-D *** learning algorithms were implementedwith a 10-fold cross-validationmethod to detect the *** highest accuracy of 90.2%was achieved using Multi-layer Perceptron(MLP)with tanh activation *** most important output of this study is the inclusion of human voice as a new feature set.

关键词： Automatic speaker recognition human voice recognition spatial pattern recognition MFCCs spectrogram machine learning artificial intelligence

Analysis and Process of Music Signals to Generate Two-Dimensional Tabular Data and a New Music

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Computers, Materials & Continua 2020年第5期63卷 553-566页

作者： Oakyoung Han Jaehyoun Kim Sungkyun Software Education Institute Sungkyunkwan UniversitySeoul03063Korea Department of Computer Education Sungkyunkwan UniversitySeoul03063Korea

The processing of sound signals is significantly improved *** for sound signal processing focusing on music beyond speech area is getting attention due to the development of deep learning *** study is for analysis and process of music signals to generate tow-dimensional tabular data and a new *** analysis and process part,we represented normalized waveforms for each of input data via frequency domain *** we looked into shorted segment to see the difference wave pattern for different *** transform is applied to get spectrogram of the music *** is applied to represent the spectrogram based on the human ear instead of the distance on the frequency dimension,and the final spectrogram has been plotted by Mel *** generating part,we created two-dimensional tabular data for data *** the 2D data,any kind of analysis can be done since it has digit values for the music ***,we generated a new music by applying LSTM toward the song audience preferred *** the result,it has been proved that the created music showed the similar waveforms with the original *** study made a step forward for music signal *** this study expands further,it can find the pattern that listeners like so music can be generated within favorite singer’s voice in the way that the listener prefers.

关键词： Frequency domain signals spectrogram fourier transform filterbank long short-term memory

Cancelable Speaker Identification System Based on Optical-Like Encryption Algorithms

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Computer Systems Science & Engineering 2022年第10期43卷 87-102页

作者： Safaa El-Gazar Walid El-Shafai Ghada El-Banby Hesham F.A.Hamed Gerges M.Salama Mohammed Abd-Elnaby Fathi E.Abd El-Samie Department of Electrical Engineering Faculty of EngineeringEgyptian-Russian UniversityCairoEgypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Biometric authentication is a rapidly growing trend that is gaining increasing attention in the last *** achieves safe access to systems using biometrics instead of the traditional *** utilization of a biometric in its original format makes it usable only ***,a cancelable biometric template should be used,so that it can be replaced when it is *** biometrics aims to enhance the security and privacy of biometric *** encryption is an efficient technique to be used in order to generate cancelable biometric *** this paper,a highly-secure encryption algorithm is proposed to ensure secure biometric data in verification *** considered biometric in this paper is the speech *** speech signal is transformed into its ***,the spectrogram is encrypted using two cascaded optical encryption *** first algorithm is the Optical Scanning Holography(OSH)for its efficiency as an encryption *** OSH encrypted spectrogram is encrypted using Double Random Phase Encoding(DRPE)by implementing two Random Phase Masks(RPMs).After the two cascaded optical encryption algorithms,the cancelable template is *** verification is implemented through correlation estimation between enrolled and test templates in their encrypted *** the correlation value is larger than a threshold value,the user is *** threshold value can be determined from the genuine and imposter correlation distribution curves as the midpoint between the two *** implementation of optical encryption is adopted using its software rather than the optical *** efficiency of the proposed cancelable biometric algorithm is illustrated by the simulation *** can improve the biometric data security without deteriorating the recognition *** results give close-to-zero This values for the Equal Error Rate(EER)and close-to-one values for the Area under Receiver Operator Charact

关键词： Cancelable biometrics spectrogram OSH DRPE EER AROC

Arabic Music Genre Classification Using Deep Convolutional Neural Networks (CNNs)

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Computers, Materials & Continua 2022年第9期72卷 5443-5458页

作者： Laiali Almazaydeh Saleh Atiewi Arar Al Tawil Khaled Elleithy Department of Software Engineering Al-Hussein Bin Talal UniversityMa’an71111Jordan Department of Computer Science Al-Hussein Bin Talal UniversityMa’an71111Jordan King Abdullah II School of Information Technology The University of JordanAmmanJordan Department of Computer Science and Engineering University of BridgeportBridgeportCT06604USA

Genres are one of the key features that categorize music based on specific series of ***,the Arabic music content on the web is poorly defined into its genres,making the automatic classification of Arabic audio genres *** this reason,in this research,our objective is first to construct a well-annotated dataset of five of the most well-known Arabic music genres,which are:Eastern Takht,Rai,Muwashshah,the poem,and Mawwal,and finally present a comprehensive empirical comparison of deep Convolutional Neural Networks(CNNs)architectures on Arabic music genres *** this work,to utilize CNNs to develop a practical classification system,the audio data is transformed into a visual representation(spectrogram)using Short Time Fast Fourier Transformation(STFT),then several audio features are extracted using Mel Frequency Cepstral Coefficients(MFCC).Performance evaluation of classifiers is measured with the accuracy score,time to build,and Matthew’s correlation coefficient(MCC).The concluded results demonstrated that AlexNet is considered among the topperforming five CNNs classifiers studied:LeNet5,AlexNet,VGG,ResNet-50,and LSTM-CNN,with an overall accuracy of 96%.

关键词： CNN MFCC spectrogram STFT arabic music genres

Raman characteristics of hydrocarbon and hydrocarbon inclusions

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Science China Earth Sciences 2007年第8期50卷 1171-1178页

作者： ZHANG Nai TIAN ZuoJi LENG YingYing WANG HuiTong SONG FuQing MENG JianHua Research Institute of Petroleum Exploration and Development Beijing 100083China Zhengyuan Geology Exploration Company of China Metallurgical Geology Bureau ShandongJinan 250014China

The Raman spectrograms of hydrocarbon standard samples show that: (1) the Raman spectrogram of normal paraffin has very strong peaks of methyl and methylene (from 2700 cm-1 to 2970 cm-1); (2) branch methyl has the particular peak of 748 cm-1±; (3) six cyclic has the particular peak of 804 cm-1±; (4) phenyl has two particular peaks of 988 cm-1± and 3058 cm-1± and the 988 cm-1± peak is stronger than the 3058 cm-1± peak; and (5) hexene has three alkenyl spectrum peaks of 1294 cm-1±, 1635 cm-1± and 2996 cm-1±, with the 1635 cm-1± peak being the strongest, showing that the number of carbon in hy-drocarbon does not affect its Raman spectrogram, and the hydrocarbon molecular structure and base groups affect its Raman spectrogram, the same hydrocarbons (such as normal paraffin) have the same Raman spectrogram; the types (such as CH4, C2H6, C3H8) and the content of hydrocarbon in oil inclu-sions are not estimated by their characteristic Raman peaks. According to the Raman spectrograms of hydrocarbon compositions, the Raman spectrogram of hydrocarbon inclusion can be divided into five types: saturated hydrocarbon Raman spectrogram, fluoresce Raman spectrogram, saturated hydro-carbon bitumen Raman spectrogram, bitumen Raman spectrogram, and ethane Raman spectrogram. And according to the characteristics of Raman spectrogram, hydrocarbon inclusions can be divided into five types: saturated hydrocarbon inclusion, less saturated hydrocarbon (oil or gas) inclusion, saturated hydrocarbon bitumen inclusion, bitumen inclusion, and methane water inclusion.

关键词： Raman Raman spectrogram oil hydrocarbon inclusion

Lower-Limb Motion-Based Ankle-Foot Movement Classification Using 2D-CNN

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Computers, Materials & Continua 2022年第10期73卷 1269-1282页

作者： Narathip Chaobankoh Tallit Jumphoo Monthippa Uthansakul Khomdet Phapatanaburi Bura Sindthupakorn Supakit Rooppakhun Peerapong Uthansakul School of Telecommunication Engineering Institute of EngineeringSuranaree University of TechnologyNakhon Ratchasima30000Thailand Department of Telecommunications Engineering Faculty of Engineering and TechnologyRajamangala University of Technology Isan(RMUTI)Nakhon Ratchasima30000Thailand Orthopedic Department School of Medicine Suranaree University of TechnologyNakhon Ratchasima30000Thailand School of Mechanical Engineering Institute of EngineeringSuranaree University of TechnologyNakhon Ratchasima30000Thailand

Recently,the Muscle-Computer Interface(MCI)has been extensively popular for employing Electromyography(EMG)signals to help the development of various assistive ***,few studies have focused on ankle foot movement classification considering EMG signals at limb *** work proposes a new framework considering two EMG signals at a lower-limb position to classify the ankle movement characteristics based on normal walking *** this purpose,we introduce a human anklefoot movement classification method using a two-dimensional-convolutional neural network(2D-CNN)with low-cost EMG sensors based on lowerlimb *** time-domain signals of EMG obtained from two sensors belonging to Dorsiflexion,Neutral-position,and Plantarflexion are firstly converted into time-frequency spectrograms by short-time Fourier ***,the spectrograms of the three ankle-foot movement types are used as input to the 2D-CNN such that the EMG foot movement types are finally *** the evaluation phase,the proposed method is investigated using the healthy volunteer for 5-fold cross-validation,and the accuracy is used as a standard *** results demonstrate that our approach provides an average accuracy of 99.34%.This exhibits the usefulness of 2D-CNN with low-cost EMG sensors in terms of ankle-foot movement classification at limb position,which offers feasibility for ***,the obtained EMG signal is not directly considered at the ankle position.

关键词： Electromyography neural network tibialis anterior muscle gastrocnemius muscle convolution neural network spectrogram lower limb

Improved Spectral Representation for Birdcall Based on Fractional Fourier Transform

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Journal of Beijing Institute of Technology 2008年第4期17卷 472-477页

作者：尹辉谢湘匡镜明 School of Information Science and Technology Beijing Institute of Technology

A novel spectral representation based on fractional Fourier transform （FrFT） is proposed and applied to birdcall analysis. The FrFT-based spectrogram of a signal is derived and compared with its FT-based counterpart, and the spectrum gathering method is used to show the energy distribution related to the pitch frequency. The fixed transform order and adaptive orders for FrFT are tested. The fixed order can be obtained empirically or calculated according to the known chirp rate. The adaptive optimal orders are determined by using ambiguity function. Experimental results with birdcalls show that the FrFT-based spectrogram with an optimal transform order has higher resolution than its STFT-based counterpart, and the better performance can be achieved if adaptive orders are used.

关键词： fractional Fourier transform (FrFT) spectrogram ambiguity function spectrum gathering

A Multi-Modal Deep Learning Approach for Emotion Recognition

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Intelligent Automation & Soft Computing 2023年第5期36卷 1561-1570页

作者： H.M.Shahzad Sohail Masood Bhatti Arfan Jaffar Muhammad Rashid The Superior University LahorePakistan National University of Technology IslamabadPakistan Intelligent Data Visual Computing Research(IDVCR) LahorePakistan

In recent years,research on facial expression recognition(FER)under mask is *** a mask for protection from Covid 19 has become a compulsion and it hides the facial expressions that is why FER under the mask is a difﬁcult *** prevailing unimodal techniques for facial recognition are not up to the mark in terms of good results for the masked face,however,a multi-modal technique can be employed to generate better *** proposed a multi-modal methodology based on deep learning for facial recognition under a masked face using facial and vocal *** multimodal has been trained on a facial and vocal *** have used two standard datasets,M-LFW for the masked dataset and CREMA-D and TESS dataset for vocal *** vocal expressions are in the form of audio while the faces data is in image form that is why the data is *** order to make the data homogeneous,the voice data is converted into images by taking spectrogram.A spectrogram embeds important features of the voice and it converts the audio format into the ***,the dataset is passed to the multimodal for *** network and the experimental results demonstrate that the proposed multimodal algorithm outsets unimodal methods and other state-of-the-art deep neural network models.

关键词： Deep learning facial expression recognition multi-model neural network speech emotion recognition spectrogram covid-19