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frequency cepstral coeffiecients</title> <meta name="description" content="Search results for: mel frequency cepstral coeffiecients"> <meta name="keywords" content="mel frequency cepstral coeffiecients"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research 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method="get" action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="mel frequency cepstral coeffiecients"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 4009</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: mel frequency cepstral coeffiecients</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4009</span> Evaluation of Features Extraction Algorithms for a Real-Time Isolated Word Recognition System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tomyslav%20Sledevi%C4%8D">Tomyslav Sledevič</a>, <a href="https://publications.waset.org/abstracts/search?q=Art%C5%ABras%20Serackis"> Artūras Serackis</a>, <a href="https://publications.waset.org/abstracts/search?q=Gintautas%20Tamulevi%C4%8Dius"> Gintautas Tamulevičius</a>, <a href="https://publications.waset.org/abstracts/search?q=Dalius%20Navakauskas"> Dalius Navakauskas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a comparative evaluation of features extraction algorithm for a real-time isolated word recognition system based on FPGA. The Mel-frequency cepstral, linear frequency cepstral, linear predictive and their cepstral coefficients were implemented in hardware/software design. The proposed system was investigated in the speaker-dependent mode for 100 different Lithuanian words. The robustness of features extraction algorithms was tested recognizing the speech records at different signals to noise rates. The experiments on clean records show highest accuracy for Mel-frequency cepstral and linear frequency cepstral coefficients. For records with 15 dB signal to noise rate the linear predictive cepstral coefficients give best result. The hard and soft part of the system is clocked on 50 MHz and 100 MHz accordingly. For the classification purpose, the pipelined dynamic time warping core was implemented. The proposed word recognition system satisfies the real-time requirements and is suitable for applications in embedded systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=isolated%20word%20recognition" title="isolated word recognition">isolated word recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=features%20extraction" title=" features extraction"> features extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=MFCC" title=" MFCC"> MFCC</a>, <a href="https://publications.waset.org/abstracts/search?q=LFCC" title=" LFCC"> LFCC</a>, <a href="https://publications.waset.org/abstracts/search?q=LPCC" title=" LPCC"> LPCC</a>, <a href="https://publications.waset.org/abstracts/search?q=LPC" title=" LPC"> LPC</a>, <a href="https://publications.waset.org/abstracts/search?q=FPGA" title=" FPGA"> FPGA</a>, <a href="https://publications.waset.org/abstracts/search?q=DTW" title=" DTW"> DTW</a> </p> <a href="https://publications.waset.org/abstracts/2136/evaluation-of-features-extraction-algorithms-for-a-real-time-isolated-word-recognition-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2136.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">496</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4008</span> Biosignal Recognition for Personal Identification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hadri%20Hussain">Hadri Hussain</a>, <a href="https://publications.waset.org/abstracts/search?q=M.Nasir%20Ibrahim"> M.Nasir Ibrahim</a>, <a href="https://publications.waset.org/abstracts/search?q=Chee-Ming%20Ting"> Chee-Ming Ting</a>, <a href="https://publications.waset.org/abstracts/search?q=Mariani%20Idroas"> Mariani Idroas</a>, <a href="https://publications.waset.org/abstracts/search?q=Fuad%20Numan"> Fuad Numan</a>, <a href="https://publications.waset.org/abstracts/search?q=Alias%20Mohd%20Noor"> Alias Mohd Noor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A biometric security system has become an important application in client identification and verification system. A conventional biometric system is normally based on unimodal biometric that depends on either behavioural or physiological information for authentication purposes. The behavioural biometric depends on human body biometric signal (such as speech) and biosignal biometric (such as electrocardiogram (ECG) and phonocardiogram or heart sound (HS)). The speech signal is commonly used in a recognition system in biometric, while the ECG and the HS have been used to identify a person’s diseases uniquely related to its cluster. However, the conventional biometric system is liable to spoof attack that will affect the performance of the system. Therefore, a multimodal biometric security system is developed, which is based on biometric signal of ECG, HS, and speech. The biosignal data involved in the biometric system is initially segmented, with each segment Mel Frequency Cepstral Coefficients (MFCC) method is exploited for extracting the feature. The Hidden Markov Model (HMM) is used to model the client and to classify the unknown input with respect to the modal. The recognition system involved training and testing session that is known as client identification (CID). In this project, twenty clients are tested with the developed system. The best overall performance at 44 kHz was 93.92% for ECG and the worst overall performance was ECG at 88.47%. The results were compared to the best overall performance at 44 kHz for (20clients) to increment of clients, which was 90.00% for HS and the worst overall performance falls at ECG at 79.91%. It can be concluded that the difference multimodal biometric has a substantial effect on performance of the biometric system and with the increment of data, even with higher frequency sampling, the performance still decreased slightly as predicted. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title="electrocardiogram">electrocardiogram</a>, <a href="https://publications.waset.org/abstracts/search?q=phonocardiogram" title=" phonocardiogram"> phonocardiogram</a>, <a href="https://publications.waset.org/abstracts/search?q=hidden%20markov%20model" title=" hidden markov model"> hidden markov model</a>, <a href="https://publications.waset.org/abstracts/search?q=mel%20frequency%20cepstral%20coeffiecients" title=" mel frequency cepstral coeffiecients"> mel frequency cepstral coeffiecients</a>, <a href="https://publications.waset.org/abstracts/search?q=client%20identification" title=" client identification"> client identification</a> </p> <a href="https://publications.waset.org/abstracts/48382/biosignal-recognition-for-personal-identification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48382.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">280</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4007</span> The Combination of the Mel Frequency Cepstral Coefficients (MFCC), Perceptual Linear Prediction (PLP), JITTER and SHIMMER Coefficients for the Improvement of Automatic Recognition System for Dysarthric Speech</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Brahim-Fares%20Zaidi">Brahim-Fares Zaidi</a>, <a href="https://publications.waset.org/abstracts/search?q=Malika%20Boudraa"> Malika Boudraa</a>, <a href="https://publications.waset.org/abstracts/search?q=Sid-Ahmed%20Selouani"> Sid-Ahmed Selouani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Our work aims to improve our Automatic Recognition System for Dysarthria Speech (ARSDS) based on the Hidden Models of Markov (HMM) and the Hidden Markov Model Toolkit (HTK) to help people who are sick. With pronunciation problems, we applied two techniques of speech parameterization based on Mel Frequency Cepstral Coefficients (MFCC's) and Perceptual Linear Prediction (PLP's) and concatenated them with JITTER and SHIMMER coefficients in order to increase the recognition rate of a dysarthria speech. For our tests, we used the NEMOURS database that represents speakers with dysarthria and normal speakers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hidden%20Markov%20model%20toolkit%20%28HTK%29" title="hidden Markov model toolkit (HTK)">hidden Markov model toolkit (HTK)</a>, <a href="https://publications.waset.org/abstracts/search?q=hidden%20models%20of%20Markov%20%28HMM%29" title=" hidden models of Markov (HMM)"> hidden models of Markov (HMM)</a>, <a href="https://publications.waset.org/abstracts/search?q=Mel-frequency%20cepstral%20coefficients%20%28MFCC%29" title=" Mel-frequency cepstral coefficients (MFCC)"> Mel-frequency cepstral coefficients (MFCC)</a>, <a href="https://publications.waset.org/abstracts/search?q=perceptual%20linear%20prediction%20%28PLP%E2%80%99s%29" title=" perceptual linear prediction (PLP’s)"> perceptual linear prediction (PLP’s)</a> </p> <a href="https://publications.waset.org/abstracts/143303/the-combination-of-the-mel-frequency-cepstral-coefficients-mfcc-perceptual-linear-prediction-plp-jitter-and-shimmer-coefficients-for-the-improvement-of-automatic-recognition-system-for-dysarthric-speech" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143303.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">161</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4006</span> Robust Features for Impulsive Noisy Speech Recognition Using Relative Spectral Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hajer%20Rahali">Hajer Rahali</a>, <a href="https://publications.waset.org/abstracts/search?q=Zied%20Hajaiej"> Zied Hajaiej</a>, <a href="https://publications.waset.org/abstracts/search?q=Noureddine%20Ellouze"> Noureddine Ellouze</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The goal of speech parameterization is to extract the relevant information about what is being spoken from the audio signal. In speech recognition systems Mel-Frequency Cepstral Coefficients (MFCC) and Relative Spectral Mel-Frequency Cepstral Coefficients (RASTA-MFCC) are the two main techniques used. It will be shown in this paper that it presents some modifications to the original MFCC method. In our work the effectiveness of proposed changes to MFCC called Modified Function Cepstral Coefficients (MODFCC) were tested and compared against the original MFCC and RASTA-MFCC features. The prosodic features such as jitter and shimmer are added to baseline spectral features. The above-mentioned techniques were tested with impulsive signals under various noisy conditions within AURORA databases. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=auditory%20filter" title="auditory filter">auditory filter</a>, <a href="https://publications.waset.org/abstracts/search?q=impulsive%20noise" title=" impulsive noise"> impulsive noise</a>, <a href="https://publications.waset.org/abstracts/search?q=MFCC" title=" MFCC"> MFCC</a>, <a href="https://publications.waset.org/abstracts/search?q=prosodic%20features" title=" prosodic features"> prosodic features</a>, <a href="https://publications.waset.org/abstracts/search?q=RASTA%20filter" title=" RASTA filter"> RASTA filter</a> </p> <a href="https://publications.waset.org/abstracts/8911/robust-features-for-impulsive-noisy-speech-recognition-using-relative-spectral-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8911.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">425</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4005</span> The Capacity of Mel Frequency Cepstral Coefficients for Speech Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fawaz%20S.%20Al-Anzi">Fawaz S. Al-Anzi</a>, <a href="https://publications.waset.org/abstracts/search?q=Dia%20AbuZeina"> Dia AbuZeina</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Speech recognition is of an important contribution in promoting new technologies in human computer interaction. Today, there is a growing need to employ speech technology in daily life and business activities. However, speech recognition is a challenging task that requires different stages before obtaining the desired output. Among automatic speech recognition (ASR) components is the feature extraction process, which parameterizes the speech signal to produce the corresponding feature vectors. Feature extraction process aims at approximating the linguistic content that is conveyed by the input speech signal. In speech processing field, there are several methods to extract speech features, however, Mel Frequency Cepstral Coefficients (MFCC) is the popular technique. It has been long observed that the MFCC is dominantly used in the well-known recognizers such as the Carnegie Mellon University (CMU) Sphinx and the Markov Model Toolkit (HTK). Hence, this paper focuses on the MFCC method as the standard choice to identify the different speech segments in order to obtain the language phonemes for further training and decoding steps. Due to MFCC good performance, the previous studies show that the MFCC dominates the Arabic ASR research. In this paper, we demonstrate MFCC as well as the intermediate steps that are performed to get these coefficients using the HTK toolkit. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=speech%20recognition" title="speech recognition">speech recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=acoustic%20features" title=" acoustic features"> acoustic features</a>, <a href="https://publications.waset.org/abstracts/search?q=mel%20frequency" title=" mel frequency"> mel frequency</a>, <a href="https://publications.waset.org/abstracts/search?q=cepstral%20coefficients" title=" cepstral coefficients"> cepstral coefficients</a> </p> <a href="https://publications.waset.org/abstracts/78382/the-capacity-of-mel-frequency-cepstral-coefficients-for-speech-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78382.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">259</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4004</span> The Combination of the Mel Frequency Cepstral Coefficients, Perceptual Linear Prediction, Jitter and Shimmer Coefficients for the Improvement of Automatic Recognition System for Dysarthric Speech</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Brahim%20Fares%20Zaidi">Brahim Fares Zaidi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Our work aims to improve our Automatic Recognition System for Dysarthria Speech based on the Hidden Models of Markov and the Hidden Markov Model Toolkit to help people who are sick. With pronunciation problems, we applied two techniques of speech parameterization based on Mel Frequency Cepstral Coefficients and Perceptual Linear Prediction and concatenated them with JITTER and SHIMMER coefficients in order to increase the recognition rate of a dysarthria speech. For our tests, we used the NEMOURS database that represents speakers with dysarthria and normal speakers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ARSDS" title="ARSDS">ARSDS</a>, <a href="https://publications.waset.org/abstracts/search?q=HTK" title=" HTK"> HTK</a>, <a href="https://publications.waset.org/abstracts/search?q=HMM" title=" HMM"> HMM</a>, <a href="https://publications.waset.org/abstracts/search?q=MFCC" title=" MFCC"> MFCC</a>, <a href="https://publications.waset.org/abstracts/search?q=PLP" title=" PLP"> PLP</a> </p> <a href="https://publications.waset.org/abstracts/158636/the-combination-of-the-mel-frequency-cepstral-coefficients-perceptual-linear-prediction-jitter-and-shimmer-coefficients-for-the-improvement-of-automatic-recognition-system-for-dysarthric-speech" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158636.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">108</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4003</span> The Optimum Mel-Frequency Cepstral Coefficients (MFCCs) Contribution to Iranian Traditional Music Genre Classification by Instrumental Features</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Abbasi%20Layegh">M. Abbasi Layegh</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Haghipour"> S. Haghipour</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Athari"> K. Athari</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Khosravi"> R. Khosravi</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Tafkikialamdari"> M. Tafkikialamdari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An approach to find the optimum mel-frequency cepstral coefficients (MFCCs) for the Radif of Mirzâ Ábdollâh, which is the principal emblem and the heart of Persian music, performed by most famous Iranian masters on two Iranian stringed instruments ‘Tar’ and ‘Setar’ is proposed. While investigating the variance of MFCC for each record in themusic database of 1500 gushe of the repertoire belonging to 12 modal systems (dastgâh and âvâz), we have applied the Fuzzy C-Mean clustering algorithm on each of the 12 coefficient and different combinations of those coefficients. We have applied the same experiment while increasing the number of coefficients but the clustering accuracy remained the same. Therefore, we can conclude that the first 7 MFCCs (V-7MFCC) are enough for classification of The Radif of Mirzâ Ábdollâh. Classical machine learning algorithms such as MLP neural networks, K-Nearest Neighbors (KNN), Gaussian Mixture Model (GMM), Hidden Markov Model (HMM) and Support Vector Machine (SVM) have been employed. Finally, it can be realized that SVM shows a better performance in this study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=radif%20of%20Mirz%C3%A2%20%C3%81bdoll%C3%A2h" title="radif of Mirzâ Ábdollâh">radif of Mirzâ Ábdollâh</a>, <a href="https://publications.waset.org/abstracts/search?q=Gushe" title=" Gushe"> Gushe</a>, <a href="https://publications.waset.org/abstracts/search?q=mel%20frequency%20cepstral%20coefficients" title=" mel frequency cepstral coefficients"> mel frequency cepstral coefficients</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20c-mean%20clustering%20algorithm" title=" fuzzy c-mean clustering algorithm"> fuzzy c-mean clustering algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=k-nearest%20neighbors%20%28KNN%29" title=" k-nearest neighbors (KNN)"> k-nearest neighbors (KNN)</a>, <a href="https://publications.waset.org/abstracts/search?q=gaussian%20mixture%20model%20%28GMM%29" title=" gaussian mixture model (GMM)"> gaussian mixture model (GMM)</a>, <a href="https://publications.waset.org/abstracts/search?q=hidden%20markov%20model%20%28HMM%29" title=" hidden markov model (HMM)"> hidden markov model (HMM)</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine%20%28SVM%29" title=" support vector machine (SVM)"> support vector machine (SVM)</a> </p> <a href="https://publications.waset.org/abstracts/37296/the-optimum-mel-frequency-cepstral-coefficients-mfccs-contribution-to-iranian-traditional-music-genre-classification-by-instrumental-features" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37296.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">446</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4002</span> Terrain Classification for Ground Robots Based on Acoustic Features</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bernd%20Kiefer">Bernd Kiefer</a>, <a href="https://publications.waset.org/abstracts/search?q=Abraham%20Gebru%20Tesfay"> Abraham Gebru Tesfay</a>, <a href="https://publications.waset.org/abstracts/search?q=Dietrich%20Klakow"> Dietrich Klakow</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The motivation of our work is to detect different terrain types traversed by a robot based on acoustic data from the robot-terrain interaction. Different acoustic features and classifiers were investigated, such as Mel-frequency cepstral coefficient and Gamma-tone frequency cepstral coefficient for the feature extraction, and Gaussian mixture model and Feed forward neural network for the classification. We analyze the system’s performance by comparing our proposed techniques with some other features surveyed from distinct related works. We achieve precision and recall values between 87% and 100% per class, and an average accuracy at 95.2%. We also study the effect of varying audio chunk size in the application phase of the models and find only a mild impact on performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=acoustic%20features" title="acoustic features">acoustic features</a>, <a href="https://publications.waset.org/abstracts/search?q=autonomous%20robots" title=" autonomous robots"> autonomous robots</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=terrain%20classification" title=" terrain classification"> terrain classification</a> </p> <a href="https://publications.waset.org/abstracts/71127/terrain-classification-for-ground-robots-based-on-acoustic-features" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71127.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">369</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4001</span> Speech Emotion Recognition: A DNN and LSTM Comparison in Single and Multiple Feature Application</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thiago%20Spilborghs%20Bueno%20Meyer">Thiago Spilborghs Bueno Meyer</a>, <a href="https://publications.waset.org/abstracts/search?q=Plinio%20Thomaz%20Aquino%20Junior"> Plinio Thomaz Aquino Junior</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Through speech, which privileges the functional and interactive nature of the text, it is possible to ascertain the spatiotemporal circumstances, the conditions of production and reception of the discourse, the explicit purposes such as informing, explaining, convincing, etc. These conditions allow bringing the interaction between humans closer to the human-robot interaction, making it natural and sensitive to information. However, it is not enough to understand what is said; it is necessary to recognize emotions for the desired interaction. The validity of the use of neural networks for feature selection and emotion recognition was verified. For this purpose, it is proposed the use of neural networks and comparison of models, such as recurrent neural networks and deep neural networks, in order to carry out the classification of emotions through speech signals to verify the quality of recognition. It is expected to enable the implementation of robots in a domestic environment, such as the HERA robot from the RoboFEI@Home team, which focuses on autonomous service robots for the domestic environment. Tests were performed using only the Mel-Frequency Cepstral Coefficients, as well as tests with several characteristics of Delta-MFCC, spectral contrast, and the Mel spectrogram. To carry out the training, validation and testing of the neural networks, the eNTERFACE’05 database was used, which has 42 speakers from 14 different nationalities speaking the English language. The data from the chosen database are videos that, for use in neural networks, were converted into audios. It was found as a result, a classification of 51,969% of correct answers when using the deep neural network, when the use of the recurrent neural network was verified, with the classification with accuracy equal to 44.09%. The results are more accurate when only the Mel-Frequency Cepstral Coefficients are used for the classification, using the classifier with the deep neural network, and in only one case, it is possible to observe a greater accuracy by the recurrent neural network, which occurs in the use of various features and setting 73 for batch size and 100 training epochs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=emotion%20recognition" title="emotion recognition">emotion recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=speech" title=" speech"> speech</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=human-robot%20interaction" title=" human-robot interaction"> human-robot interaction</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a> </p> <a href="https://publications.waset.org/abstracts/145908/speech-emotion-recognition-a-dnn-and-lstm-comparison-in-single-and-multiple-feature-application" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145908.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">170</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4000</span> Heuristic Classification of Hydrophone Recordings</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Daniel%20M.%20Wolff">Daniel M. Wolff</a>, <a href="https://publications.waset.org/abstracts/search?q=Patricia%20Gray"> Patricia Gray</a>, <a href="https://publications.waset.org/abstracts/search?q=Rafael%20de%20la%20Parra%20Venegas"> Rafael de la Parra Venegas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An unsupervised machine listening system is constructed and applied to a dataset of 17,195 30-second marine hydrophone recordings. The system is then heuristically supplemented with anecdotal listening, contextual recording information, and supervised learning techniques to reduce the number of false positives. Features for classification are assembled by extracting the following data from each of the audio files: the spectral centroid, root-mean-squared values for each frequency band of a 10-octave filter bank, and mel-frequency cepstral coefficients in 5-second frames. In this way both time- and frequency-domain information are contained in the features to be passed to a clustering algorithm. Classification is performed using the k-means algorithm and then a k-nearest neighbors search. Different values of k are experimented with, in addition to different combinations of the available feature sets. Hypothesized class labels are 'primarily anthrophony' and 'primarily biophony', where the best class result conforming to the former label has 104 members after heuristic pruning. This demonstrates how a large audio dataset has been made more tractable with machine learning techniques, forming the foundation of a framework designed to acoustically monitor and gauge biological and anthropogenic activity in a marine environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=anthrophony" title="anthrophony">anthrophony</a>, <a href="https://publications.waset.org/abstracts/search?q=hydrophone" title=" hydrophone"> hydrophone</a>, <a href="https://publications.waset.org/abstracts/search?q=k-means" title=" k-means"> k-means</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/89273/heuristic-classification-of-hydrophone-recordings" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89273.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">170</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3999</span> Music Genre Classification Based on Non-Negative Matrix Factorization Features</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Soyon%20Kim">Soyon Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Edward%20Kim"> Edward Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to retrieve information from the massive stream of songs in the music industry, music search by title, lyrics, artist, mood, and genre has become more important. Despite the subjectivity and controversy over the definition of music genres across different nations and cultures, automatic genre classification systems that facilitate the process of music categorization have been developed. Manual genre selection by music producers is being provided as statistical data for designing automatic genre classification systems. In this paper, an automatic music genre classification system utilizing non-negative matrix factorization (NMF) is proposed. Short-term characteristics of the music signal can be captured based on the timbre features such as mel-frequency cepstral coefficient (MFCC), decorrelated filter bank (DFB), octave-based spectral contrast (OSC), and octave band sum (OBS). Long-term time-varying characteristics of the music signal can be summarized with (1) the statistical features such as mean, variance, minimum, and maximum of the timbre features and (2) the modulation spectrum features such as spectral flatness measure, spectral crest measure, spectral peak, spectral valley, and spectral contrast of the timbre features. Not only these conventional basic long-term feature vectors, but also NMF based feature vectors are proposed to be used together for genre classification. In the training stage, NMF basis vectors were extracted for each genre class. The NMF features were calculated in the log spectral magnitude domain (NMF-LSM) as well as in the basic feature vector domain (NMF-BFV). For NMF-LSM, an entire full band spectrum was used. However, for NMF-BFV, only low band spectrum was used since high frequency modulation spectrum of the basic feature vectors did not contain important information for genre classification. In the test stage, using the set of pre-trained NMF basis vectors, the genre classification system extracted the NMF weighting values of each genre as the NMF feature vectors. A support vector machine (SVM) was used as a classifier. The GTZAN multi-genre music database was used for training and testing. It is composed of 10 genres and 100 songs for each genre. To increase the reliability of the experiments, 10-fold cross validation was used. For a given input song, an extracted NMF-LSM feature vector was composed of 10 weighting values that corresponded to the classification probabilities for 10 genres. An NMF-BFV feature vector also had a dimensionality of 10. Combined with the basic long-term features such as statistical features and modulation spectrum features, the NMF features provided the increased accuracy with a slight increase in feature dimensionality. The conventional basic features by themselves yielded 84.0% accuracy, but the basic features with NMF-LSM and NMF-BFV provided 85.1% and 84.2% accuracy, respectively. The basic features required dimensionality of 460, but NMF-LSM and NMF-BFV required dimensionalities of 10 and 10, respectively. Combining the basic features, NMF-LSM and NMF-BFV together with the SVM with a radial basis function (RBF) kernel produced the significantly higher classification accuracy of 88.3% with a feature dimensionality of 480. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mel-frequency%20cepstral%20coefficient%20%28MFCC%29" title="mel-frequency cepstral coefficient (MFCC)">mel-frequency cepstral coefficient (MFCC)</a>, <a href="https://publications.waset.org/abstracts/search?q=music%20genre%20classification" title=" music genre classification"> music genre classification</a>, <a href="https://publications.waset.org/abstracts/search?q=non-negative%20matrix%20factorization%20%28NMF%29" title=" non-negative matrix factorization (NMF)"> non-negative matrix factorization (NMF)</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine%20%28SVM%29" title=" support vector machine (SVM)"> support vector machine (SVM)</a> </p> <a href="https://publications.waset.org/abstracts/89349/music-genre-classification-based-on-non-negative-matrix-factorization-features" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89349.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">303</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3998</span> Robustness of the Deep Chroma Extractor and Locally-Normalized Quarter Tone Filters in Automatic Chord Estimation under Reverberant Conditions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Luis%20Alvarado">Luis Alvarado</a>, <a href="https://publications.waset.org/abstracts/search?q=Victor%20Poblete"> Victor Poblete</a>, <a href="https://publications.waset.org/abstracts/search?q=Isaac%20Gonzalez"> Isaac Gonzalez</a>, <a href="https://publications.waset.org/abstracts/search?q=Yetzabeth%20Gonzalez"> Yetzabeth Gonzalez</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In MIREX 2016 (http://www.music-ir.org/mirex), the deep neural network (DNN)-Deep Chroma Extractor, proposed by Korzeniowski and Wiedmer, reached the highest score in an audio chord recognition task. In the present paper, this tool is assessed under acoustic reverberant environments and distinct source-microphone distances. The evaluation dataset comprises The Beatles and Queen datasets. These datasets are sequentially re-recorded with a single microphone in a real reverberant chamber at four reverberation times (0 -anechoic-, 1, 2, and 3 s, approximately), as well as four source-microphone distances (32, 64, 128, and 256 cm). It is expected that the performance of the trained DNN will dramatically decrease under these acoustic conditions with signals degraded by room reverberation and distance to the source. Recently, the effect of the bio-inspired Locally-Normalized Cepstral Coefficients (LNCC), has been assessed in a text independent speaker verification task using speech signals degraded by additive noise at different signal-to-noise ratios with variations of recording distance, and it has also been assessed under reverberant conditions with variations of recording distance. LNCC showed a performance so high as the state-of-the-art Mel Frequency Cepstral Coefficient filters. Based on these results, this paper proposes a variation of locally-normalized triangular filters called Locally-Normalized Quarter Tone (LNQT) filters. By using the LNQT spectrogram, robustness improvements of the trained Deep Chroma Extractor are expected, compared with classical triangular filters, and thus compensating the music signal degradation improving the accuracy of the chord recognition system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chord%20recognition" title="chord recognition">chord recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20neural%20networks" title=" deep neural networks"> deep neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=music%20information%20retrieval" title=" music information retrieval"> music information retrieval</a> </p> <a href="https://publications.waset.org/abstracts/92608/robustness-of-the-deep-chroma-extractor-and-locally-normalized-quarter-tone-filters-in-automatic-chord-estimation-under-reverberant-conditions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92608.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">232</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3997</span> Road Vehicle Recognition Using Magnetic Sensing Feature Extraction and Classification </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiao%20Chen">Xiao Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaoying%20Kong"> Xiaoying Kong</a>, <a href="https://publications.waset.org/abstracts/search?q=Min%20Xu"> Min Xu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a road vehicle detection approach for the intelligent transportation system. This approach mainly uses low-cost magnetic sensor and associated data collection system to collect magnetic signals. This system can measure the magnetic field changing, and it also can detect and count vehicles. We extend Mel Frequency Cepstral Coefficients to analyze vehicle magnetic signals. Vehicle type features are extracted using representation of cepstrum, frame energy, and gap cepstrum of magnetic signals. We design a 2-dimensional map algorithm using Vector Quantization to classify vehicle magnetic features to four typical types of vehicles in Australian suburbs: sedan, VAN, truck, and bus. Experiments results show that our approach achieves a high level of accuracy for vehicle detection and classification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=vehicle%20classification" title="vehicle classification">vehicle classification</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20processing" title=" signal processing"> signal processing</a>, <a href="https://publications.waset.org/abstracts/search?q=road%20traffic%20model" title=" road traffic model"> road traffic model</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic%20sensing" title=" magnetic sensing"> magnetic sensing</a> </p> <a href="https://publications.waset.org/abstracts/86644/road-vehicle-recognition-using-magnetic-sensing-feature-extraction-and-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/86644.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">320</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3996</span> Wolof Voice Response Recognition System: A Deep Learning Model for Wolof Audio Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Krishna%20Mohan%20Bathula">Krishna Mohan Bathula</a>, <a href="https://publications.waset.org/abstracts/search?q=Fatou%20Bintou%20Loucoubar"> Fatou Bintou Loucoubar</a>, <a href="https://publications.waset.org/abstracts/search?q=FNU%20Kaleemunnisa"> FNU Kaleemunnisa</a>, <a href="https://publications.waset.org/abstracts/search?q=Christelle%20Scharff"> Christelle Scharff</a>, <a href="https://publications.waset.org/abstracts/search?q=Mark%20Anthony%20De%20Castro"> Mark Anthony De Castro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Voice recognition algorithms such as automatic speech recognition and text-to-speech systems with African languages can play an important role in bridging the digital divide of Artificial Intelligence in Africa, contributing to the establishment of a fully inclusive information society. This paper proposes a Deep Learning model that can classify the user responses as inputs for an interactive voice response system. A dataset with Wolof language words ‘yes’ and ‘no’ is collected as audio recordings. A two stage Data Augmentation approach is adopted for enhancing the dataset size required by the deep neural network. Data preprocessing and feature engineering with Mel-Frequency Cepstral Coefficients are implemented. Convolutional Neural Networks (CNNs) have proven to be very powerful in image classification and are promising for audio processing when sounds are transformed into spectra. For performing voice response classification, the recordings are transformed into sound frequency feature spectra and then applied image classification methodology using a deep CNN model. The inference model of this trained and reusable Wolof voice response recognition system can be integrated with many applications associated with both web and mobile platforms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automatic%20speech%20recognition" title="automatic speech recognition">automatic speech recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=interactive%20voice%20response" title=" interactive voice response"> interactive voice response</a>, <a href="https://publications.waset.org/abstracts/search?q=voice%20response%20recognition" title=" voice response recognition"> voice response recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=wolof%20word%20classification" title=" wolof word classification"> wolof word classification</a> </p> <a href="https://publications.waset.org/abstracts/150305/wolof-voice-response-recognition-system-a-deep-learning-model-for-wolof-audio-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150305.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">116</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3995</span> Comparison of Frequency-Domain Contention Schemes in Wireless LANs </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Li%20Feng">Li Feng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In IEEE 802.11 networks, it is well known that the traditional time-domain contention often leads to low channel utilization. The first frequency-domain contention scheme, the time to frequency (T2F), has recently been proposed to improve the channel utilization and has attracted a great deal of attention. In this paper, we survey the latest research progress on the weighed frequency-domain contention. We present the basic ideas, work principles of these related schemes and point out their differences. This paper is very useful for further study on frequency-domain contention. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=802.11" title="802.11">802.11</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20LANs" title=" wireless LANs"> wireless LANs</a>, <a href="https://publications.waset.org/abstracts/search?q=frequency-domain%20contention" title=" frequency-domain contention"> frequency-domain contention</a>, <a href="https://publications.waset.org/abstracts/search?q=T2F" title=" T2F"> T2F</a> </p> <a href="https://publications.waset.org/abstracts/42959/comparison-of-frequency-domain-contention-schemes-in-wireless-lans" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42959.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">459</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3994</span> Comparative Methods for Speech Enhancement and the Effects on Text-Independent Speaker Identification Performance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Ajgou">R. Ajgou</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Sbaa"> S. Sbaa</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Ghendir"> S. Ghendir</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Chemsa"> A. Chemsa</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Taleb-Ahmed"> A. Taleb-Ahmed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The speech enhancement algorithm is to improve speech quality. In this paper, we review some speech enhancement methods and we evaluated their performance based on Perceptual Evaluation of Speech Quality scores (PESQ, ITU-T P.862). All method was evaluated in presence of different kind of noise using TIMIT database and NOIZEUS noisy speech corpus.. The noise was taken from the AURORA database and includes suburban train noise, babble, car, exhibition hall, restaurant, street, airport and train station noise. Simulation results showed improved performance of speech enhancement for Tracking of non-stationary noise approach in comparison with various methods in terms of PESQ measure. Moreover, we have evaluated the effects of the speech enhancement technique on Speaker Identification system based on autoregressive (AR) model and Mel-frequency Cepstral coefficients (MFCC). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=speech%20enhancement" title="speech enhancement">speech enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=pesq" title=" pesq"> pesq</a>, <a href="https://publications.waset.org/abstracts/search?q=speaker%20recognition" title=" speaker recognition"> speaker recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=MFCC" title=" MFCC"> MFCC</a> </p> <a href="https://publications.waset.org/abstracts/31102/comparative-methods-for-speech-enhancement-and-the-effects-on-text-independent-speaker-identification-performance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31102.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">424</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3993</span> Experimental Investigation on the Optimal Operating Frequency of a Thermoacoustic Refrigerator</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kriengkrai%20Assawamartbunlue">Kriengkrai Assawamartbunlue</a>, <a href="https://publications.waset.org/abstracts/search?q=Channarong%20Wantha"> Channarong Wantha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the effects of the mean operating pressure on the optimal operating frequency based on temperature differences across stack ends in a thermoacoustic refrigerator. In addition to the length of the resonance tube, components of the thermoacoustic refrigerator have an influence on the operating frequency due to their acoustic properties, i.e. absorptivity, reflectivity and transmissivity. The interference of waves incurs and distorts the original frequency generated by the driver so that the optimal operating frequency differs from the designs. These acoustic properties are not parameters in the designs and it is very complicated to infer their responses. A prototype thermoacoustic refrigerator is constructed and used to investigate its optimal operating frequency compared to the design at various operating pressures. Helium and air are used as working fluids during the experiments. The results indicate that the optimal operating frequency of the prototype thermoacoustic refrigerator using helium is at 6 bar and 490Hz or approximately 20% away from the design frequency. The optimal operating frequency at other mean pressures differs from the design in an unpredictable manner, however, the optimal operating frequency and pressure can be identified by testing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=acoustic%20properties" title="acoustic properties">acoustic properties</a>, <a href="https://publications.waset.org/abstracts/search?q=Carnot%E2%80%99s%20efficiency" title=" Carnot’s efficiency"> Carnot’s efficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=interference%20of%20waves" title=" interference of waves"> interference of waves</a>, <a href="https://publications.waset.org/abstracts/search?q=operating%20pressure" title=" operating pressure"> operating pressure</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20operating%20frequency" title=" optimal operating frequency"> optimal operating frequency</a>, <a href="https://publications.waset.org/abstracts/search?q=stack%20performance" title=" stack performance"> stack performance</a>, <a href="https://publications.waset.org/abstracts/search?q=standing%20wave" title=" standing wave"> standing wave</a>, <a href="https://publications.waset.org/abstracts/search?q=thermoacoustic%20refrigerator" title=" thermoacoustic refrigerator"> thermoacoustic refrigerator</a> </p> <a href="https://publications.waset.org/abstracts/23908/experimental-investigation-on-the-optimal-operating-frequency-of-a-thermoacoustic-refrigerator" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23908.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">486</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3992</span> Investigation of the Effects of Sampling Frequency on the THD of 3-Phase Inverters Using Space Vector Modulation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khattab%20Al%20Qaisi">Khattab Al Qaisi</a>, <a href="https://publications.waset.org/abstracts/search?q=Nicholas%20Bowring"> Nicholas Bowring</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the simulation results of the effects of sampling frequency on the total harmonic distortion (THD) of three-phase inverters using the space vector pulse width modulation (SVPWM) and space vector control (SVC) algorithms. The relationship between the variables was studied using curve fitting techniques, and it has been shown that, for 50 Hz inverters, there is an exponential relation between the sampling frequency and THD up to around 8500 Hz, beyond which the performance of the model becomes irregular, and there is an negative exponential relation between the sampling frequency and the marginal improvement to the THD. It has also been found that the performance of SVPWM is better than that of SVC with the same sampling frequency in most frequency range, including the range where the performance of the former is irregular. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DSI" title="DSI">DSI</a>, <a href="https://publications.waset.org/abstracts/search?q=SVPWM" title=" SVPWM"> SVPWM</a>, <a href="https://publications.waset.org/abstracts/search?q=THD" title=" THD"> THD</a>, <a href="https://publications.waset.org/abstracts/search?q=DC-AC%20converter" title=" DC-AC converter"> DC-AC converter</a>, <a href="https://publications.waset.org/abstracts/search?q=sampling%20frequency" title=" sampling frequency"> sampling frequency</a>, <a href="https://publications.waset.org/abstracts/search?q=performance" title=" performance"> performance</a> </p> <a href="https://publications.waset.org/abstracts/17856/investigation-of-the-effects-of-sampling-frequency-on-the-thd-of-3-phase-inverters-using-space-vector-modulation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17856.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">485</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3991</span> Determining Efficiency of Frequency Control System of Karkheh Power Plant in Main Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ferydon%20Salehifar">Ferydon Salehifar</a>, <a href="https://publications.waset.org/abstracts/search?q=Hassan%20Safarikia"> Hassan Safarikia</a>, <a href="https://publications.waset.org/abstracts/search?q=Hossein%20Boromandfar"> Hossein Boromandfar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Karkheh plant in Iran's Khuzestan province and is located in the city Andimeshk. The plant has a production capacity of 400 MW units with water and three hours. One of the important parameters of each country's power grid stability is the stability of the power grid is affected by the voltage and frequency In plants, the amount of active power frequency control is done so that when the unit is placed in the frequency control their productivity is a function of frequency and output power varies with frequency. Produced by hydroelectric power plants with the water level behind the dam has a direct relationship And to decrease and increase the water level behind the dam in order to reduce the power output increases But these changes have a different interval is due to some mechanical problems such as turbine cavitation and vibration are limited. In this study, the range of the frequency control can be Karkheh manufacturing plants have been identified and their effectiveness has been determined. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Karkheh%20power" title="Karkheh power">Karkheh power</a>, <a href="https://publications.waset.org/abstracts/search?q=frequency%20control%20system" title=" frequency control system"> frequency control system</a>, <a href="https://publications.waset.org/abstracts/search?q=active%20power" title=" active power"> active power</a>, <a href="https://publications.waset.org/abstracts/search?q=efficiency" title=" efficiency"> efficiency</a> </p> <a href="https://publications.waset.org/abstracts/25787/determining-efficiency-of-frequency-control-system-of-karkheh-power-plant-in-main-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25787.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">620</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3990</span> Optimal ECG Sampling Frequency for Multiscale Entropy-Based HRV</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Manjit%20Singh">Manjit Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Multiscale entropy (MSE) is an extensively used index to provide a general understanding of multiple complexity of physiologic mechanism of heart rate variability (HRV) that operates on a wide range of time scales. Accurate selection of electrocardiogram (ECG) sampling frequency is an essential concern for clinically significant HRV quantification; high ECG sampling rate increase memory requirements and processing time, whereas low sampling rate degrade signal quality and results in clinically misinterpreted HRV. In this work, the impact of ECG sampling frequency on MSE based HRV have been quantified. MSE measures are found to be sensitive to ECG sampling frequency and effect of sampling frequency will be a function of time scale. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ECG%20%28electrocardiogram%29" title="ECG (electrocardiogram)">ECG (electrocardiogram)</a>, <a href="https://publications.waset.org/abstracts/search?q=heart%20rate%20variability%20%28HRV%29" title=" heart rate variability (HRV)"> heart rate variability (HRV)</a>, <a href="https://publications.waset.org/abstracts/search?q=multiscale%20entropy" title=" multiscale entropy"> multiscale entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=sampling%20frequency" title=" sampling frequency"> sampling frequency</a> </p> <a href="https://publications.waset.org/abstracts/78603/optimal-ecg-sampling-frequency-for-multiscale-entropy-based-hrv" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78603.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">271</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3989</span> Dynamic Response of Structure-Raft-Pile-Soil with Respect to System Frequency</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Razmi">B. Razmi</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Rafiee"> F. Rafiee</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Baziar"> M. Baziar</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Saeedi%20Azizkandi"> A. Saeedi Azizkandi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the present research, a series of 3-D finite element numerical modeling was performed to study the effect of system frequency and excitation specifications on the internal forces of the piled raft (PR) system in a dry sand layer. The results of numerical simulations were first compared with those associated with centrifuge tests. The natural frequency of superstructure, modeled on the piled raft foundation, was smaller than the natural frequency of the fixed-base super-structure. This difference was greater for super-structures with higher frequencies. In PR systems, the excitation with a frequency close to the system frequency produced the largest responses. Furthermore, based on the results of presented numerical analyses, ignoring the interactions and characteristics of all components of a pile-raft-structure, may lead to highly uneconomical design. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=centrifuge%20test" title="centrifuge test">centrifuge test</a>, <a href="https://publications.waset.org/abstracts/search?q=excitation%20frequency" title=" excitation frequency"> excitation frequency</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20frequency%20of%20super-structure" title=" natural frequency of super-structure"> natural frequency of super-structure</a>, <a href="https://publications.waset.org/abstracts/search?q=piled%20raft%20foundation" title=" piled raft foundation"> piled raft foundation</a>, <a href="https://publications.waset.org/abstracts/search?q=3-D%20finite%20element%20model" title=" 3-D finite element model"> 3-D finite element model</a> </p> <a href="https://publications.waset.org/abstracts/150187/dynamic-response-of-structure-raft-pile-soil-with-respect-to-system-frequency" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150187.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">117</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3988</span> Forensic Speaker Verification in Noisy Environmental by Enhancing the Speech Signal Using ICA Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Kamil%20Hasan%20Al-Ali">Ahmed Kamil Hasan Al-Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Bouchra%20Senadji"> Bouchra Senadji</a>, <a href="https://publications.waset.org/abstracts/search?q=Ganesh%20Naik"> Ganesh Naik</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We propose a system to real environmental noise and channel mismatch for forensic speaker verification systems. This method is based on suppressing various types of real environmental noise by using independent component analysis (ICA) algorithm. The enhanced speech signal is applied to mel frequency cepstral coefficients (MFCC) or MFCC feature warping to extract the essential characteristics of the speech signal. Channel effects are reduced using an intermediate vector (i-vector) and probabilistic linear discriminant analysis (PLDA) approach for classification. The proposed algorithm is evaluated by using an Australian forensic voice comparison database, combined with car, street and home noises from QUT-NOISE at a signal to noise ratio (SNR) ranging from -10 dB to 10 dB. Experimental results indicate that the MFCC feature warping-ICA achieves a reduction in equal error rate about (48.22%, 44.66%, and 50.07%) over using MFCC feature warping when the test speech signals are corrupted with random sessions of street, car, and home noises at -10 dB SNR. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=noisy%20forensic%20speaker%20verification" title="noisy forensic speaker verification">noisy forensic speaker verification</a>, <a href="https://publications.waset.org/abstracts/search?q=ICA%20algorithm" title=" ICA algorithm"> ICA algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=MFCC" title=" MFCC"> MFCC</a>, <a href="https://publications.waset.org/abstracts/search?q=MFCC%20feature%20warping" title=" MFCC feature warping"> MFCC feature warping</a> </p> <a href="https://publications.waset.org/abstracts/66332/forensic-speaker-verification-in-noisy-environmental-by-enhancing-the-speech-signal-using-ica-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66332.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">408</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3987</span> Frequency Transformation with Pascal Matrix Equations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Phuoc%20Si%20Nguyen">Phuoc Si Nguyen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Frequency transformation with Pascal matrix equations is a method for transforming an electronic filter (analogue or digital) into another filter. The technique is based on frequency transformation in the s-domain, bilinear z-transform with pre-warping frequency, inverse bilinear transformation and a very useful application of the Pascal’s triangle that simplifies computing and enables calculation by hand when transforming from one filter to another. This paper will introduce two methods to transform a filter into a digital filter: frequency transformation from the s-domain into the z-domain; and frequency transformation in the z-domain. Further, two Pascal matrix equations are derived: an analogue to digital filter Pascal matrix equation and a digital to digital filter Pascal matrix equation. These are used to design a desired digital filter from a given filter. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=frequency%20transformation" title="frequency transformation">frequency transformation</a>, <a href="https://publications.waset.org/abstracts/search?q=bilinear%20z-transformation" title=" bilinear z-transformation"> bilinear z-transformation</a>, <a href="https://publications.waset.org/abstracts/search?q=pre-warping%20frequency" title=" pre-warping frequency"> pre-warping frequency</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20filters" title=" digital filters"> digital filters</a>, <a href="https://publications.waset.org/abstracts/search?q=analog%20filters" title=" analog filters"> analog filters</a>, <a href="https://publications.waset.org/abstracts/search?q=pascal%E2%80%99s%20triangle" title=" pascal’s triangle"> pascal’s triangle</a> </p> <a href="https://publications.waset.org/abstracts/34866/frequency-transformation-with-pascal-matrix-equations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34866.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">549</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3986</span> Dielectric Properties of Ni-Al Nano Ferrites Synthesized by Citrate Gel Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=D.%20Ravinder">D. Ravinder</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20S.%20Nagaraju"> K. S. Nagaraju </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ni–Al ferrite with composition of NiAlxFe2-xO4 (x=0.2, 0.4 0.6, and 0.8, ) were prepared by citrate gel method. The dielectric properties for all the samples were investigated at room temperature as a function of frequency. The dielectric constant shows dispersion in the lower frequency region and remains almost constant at higher frequencies. The frequency dependence of dielectric loss tangent (tanδ) is found to be abnormal, giving a peak at certain frequency for mixed Ni-Al ferrites. A qualitative explanation is given for the composition and frequency dependence of the dielectric loss tangent. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ferrites" title="ferrites">ferrites</a>, <a href="https://publications.waset.org/abstracts/search?q=citrate%20method" title=" citrate method"> citrate method</a>, <a href="https://publications.waset.org/abstracts/search?q=lattice%20parameter" title=" lattice parameter"> lattice parameter</a>, <a href="https://publications.waset.org/abstracts/search?q=dielectric%20constant" title=" dielectric constant"> dielectric constant</a> </p> <a href="https://publications.waset.org/abstracts/57941/dielectric-properties-of-ni-al-nano-ferrites-synthesized-by-citrate-gel-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57941.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">303</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3985</span> Revised Tower Earthing Design in High-Voltage Transmission Network for High-Frequency Lightning Condition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Azwadi%20Mohamad">Azwadi Mohamad</a>, <a href="https://publications.waset.org/abstracts/search?q=Pauzi%20Yahaya"> Pauzi Yahaya</a>, <a href="https://publications.waset.org/abstracts/search?q=Nadiah%20Hudi"> Nadiah Hudi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Earthing system for high-voltage transmission tower is designed to protect the working personnel and equipments, and to maintain the quality of supply during fault. The existing earthing system for transmission towers in TNB’s system is purposely designed for normal power frequency (low-frequency) fault conditions that take into account the step and touch voltages. This earthing design is found to be inapt for lightning (transient) condition to a certain extent, which involves a high-frequency domain. The current earthing practice of laying the electrodes radially in straight 60 m horizontal lines under the ground, in order to achieve the specified impedance value of less than 10 Ω, was deemed ineffective in reducing the high-frequency impedance. This paper introduces a new earthing design that produces low impedance value at the high-frequency domain, without compromising the performance of low-frequency impedance. The performances of this new earthing design, as well as the existing design, are simulated for various soil resistivity values at varying frequency. The proposed concentrated earthing design is found to possess low TFR value at both low and high-frequency. A good earthing design should have a fine balance between compact and radial electrodes under the ground. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=earthing%20design" title="earthing design">earthing design</a>, <a href="https://publications.waset.org/abstracts/search?q=high-frequency" title=" high-frequency"> high-frequency</a>, <a href="https://publications.waset.org/abstracts/search?q=lightning" title=" lightning"> lightning</a>, <a href="https://publications.waset.org/abstracts/search?q=tower%20footing%20impedance" title=" tower footing impedance"> tower footing impedance</a> </p> <a href="https://publications.waset.org/abstracts/129491/revised-tower-earthing-design-in-high-voltage-transmission-network-for-high-frequency-lightning-condition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129491.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">161</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3984</span> Signal On-Off Ratio and Output Frequency Analysis of Semiconductor Electron-Interference Device</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tomotaka%20Aoki">Tomotaka Aoki</a>, <a href="https://publications.waset.org/abstracts/search?q=Isao%20Tomita"> Isao Tomita</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We examined the on-off ratio and frequency components of output signals from an electron-interference device made of GaAs/AlₓGa₁₋ₓAs by solving the time-dependent Schrödinger's equation on conducting electrons in the channel waveguide of the device. For electron-wave modulation, a periodic voltage of frequency f was applied to the channel. Furthermore, we examined the voltage-amplitude dependence of the signals in time and frequency domains and found that large applied voltage deformed the output-signal waveform and created additional side modes (frequencies) near the modulation frequency f and that there was a trade-off between on-off ratio and side-mode creation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electrical%20conduction" title="electrical conduction">electrical conduction</a>, <a href="https://publications.waset.org/abstracts/search?q=electron%20interference" title=" electron interference"> electron interference</a>, <a href="https://publications.waset.org/abstracts/search?q=frequency%20spectrum" title=" frequency spectrum"> frequency spectrum</a>, <a href="https://publications.waset.org/abstracts/search?q=on-off%20ratio" title=" on-off ratio"> on-off ratio</a> </p> <a href="https://publications.waset.org/abstracts/145444/signal-on-off-ratio-and-output-frequency-analysis-of-semiconductor-electron-interference-device" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145444.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">121</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3983</span> Slugging Frequency Correlation for High Viscosity Oil-Gas Flow in Horizontal Pipeline </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Y.%20Danjuma">B. Y. Danjuma</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Archibong-Eso"> A. Archibong-Eso</a>, <a href="https://publications.waset.org/abstracts/search?q=Aliyu%20M.%20Aliyu"> Aliyu M. Aliyu</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Yeung"> H. Yeung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this experimental investigation, a new data for slugging frequency for high viscosity oil-gas flow are reported. Scale experiments were carried out using a mixture of air and mineral oil as the liquid phase in a 17 m long horizontal pipe with 0.0762 ID. The data set was acquired using two high-speed Gamma Densitometers at a data acquisition frequency of 250 Hz over a time interval of 30 seconds. For the range of flow conditions investigated, increase in liquid oil viscosity was observed to strongly influence the slug frequency. A comparison of the present data with prediction models available in the literature revealed huge discrepancies. A new correlation incorporating the effect of viscosity on slug frequency has been proposed for the horizontal flow, which represents the main contribution of this work. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gamma%20densitometer" title="gamma densitometer">gamma densitometer</a>, <a href="https://publications.waset.org/abstracts/search?q=flow%20pattern" title=" flow pattern"> flow pattern</a>, <a href="https://publications.waset.org/abstracts/search?q=pressure%20gradient" title=" pressure gradient"> pressure gradient</a>, <a href="https://publications.waset.org/abstracts/search?q=slug%20frequency" title=" slug frequency"> slug frequency</a> </p> <a href="https://publications.waset.org/abstracts/36688/slugging-frequency-correlation-for-high-viscosity-oil-gas-flow-in-horizontal-pipeline" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36688.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">412</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3982</span> Dielectric Properties in Frequency Domain of Main Insulation System of Printed Circuit Board</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xize%20Dai">Xize Dai</a>, <a href="https://publications.waset.org/abstracts/search?q=Jian%20Hao"> Jian Hao</a>, <a href="https://publications.waset.org/abstracts/search?q=Claus%20Leth%20Bak"> Claus Leth Bak</a>, <a href="https://publications.waset.org/abstracts/search?q=Gian%20Carlo%20Montanari"> Gian Carlo Montanari</a>, <a href="https://publications.waset.org/abstracts/search?q=Huai%20Wang"> Huai Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Printed Circuit Board (PCB) is a critical component applicable to power electronics systems, especially for high-voltage applications involving several high-voltage and high-frequency SiC/GaN devices. The insulation system of PCB is facing more challenges from high-voltage and high-frequency stress that can alter the dielectric properties. Dielectric properties of the PCB insulation system also determine the electrical field distribution that correlates with intrinsic and extrinsic aging mechanisms. Hence, investigating the dielectric properties in the frequency domain of the PCB insulation system is a must. The paper presents the frequency-dependent, temperature-dependent, and voltage-dependent dielectric properties, permittivity, conductivity, and dielectric loss tangents of PCB insulation systems. The dielectric properties mechanisms associated with frequency, temperature, and voltage are revealed from the design perspective. It can be concluded that the dielectric properties of PCB in the frequency domain show a strong dependence on voltage, frequency, and temperature. The voltage-, frequency-, and temperature-dependent dielectric properties are associated with intrinsic conduction behavior and polarization patterns from the perspective of dielectric theory. The results may provide some reference for the PCB insulation system design in high voltage, high frequency, and high-temperature power electronics applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electrical%20insulation%20system" title="electrical insulation system">electrical insulation system</a>, <a href="https://publications.waset.org/abstracts/search?q=dielectric%20properties" title=" dielectric properties"> dielectric properties</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20voltage%20and%20frequency" title=" high voltage and frequency"> high voltage and frequency</a>, <a href="https://publications.waset.org/abstracts/search?q=printed%20circuit%20board" title=" printed circuit board"> printed circuit board</a> </p> <a href="https://publications.waset.org/abstracts/168071/dielectric-properties-in-frequency-domain-of-main-insulation-system-of-printed-circuit-board" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168071.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">94</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3981</span> Parabolic Impact Law of High Frequency Exchanges on Price Formation in Commodities Market</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=L.%20Maiza">L. Maiza</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Cantagrel"> A. Cantagrel</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Forestier"> M. Forestier</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20Laucoin"> G. Laucoin</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Regali"> T. Regali</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Evaluation of High Frequency Trading (HFT) impact on financial markets is very important for traders who use market analysis to detect winning transaction opportunity. Analysis of HFT data on tobacco commodity market is discussed here and interesting linear relationship has been shown between trading frequency and difference between averaged trading prices above and below considered trading frequency. This may open new perspectives on markets data understanding and could provide possible interpretation of Adam Smith invisible hand. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=financial%20market" title="financial market">financial market</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20frequency%20trading" title=" high frequency trading"> high frequency trading</a>, <a href="https://publications.waset.org/abstracts/search?q=analysis" title=" analysis"> analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=impacts" title=" impacts"> impacts</a>, <a href="https://publications.waset.org/abstracts/search?q=Adam%20Smith%20invisible%20hand" title=" Adam Smith invisible hand"> Adam Smith invisible hand</a> </p> <a href="https://publications.waset.org/abstracts/35722/parabolic-impact-law-of-high-frequency-exchanges-on-price-formation-in-commodities-market" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35722.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">359</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3980</span> Frequency-Dependent and Full Range Tunable Phase Shifter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yufu%20Yin">Yufu Yin</a>, <a href="https://publications.waset.org/abstracts/search?q=Tao%20Lin"> Tao Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Shanghong%20Zhao"> Shanghong Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Zihang%20Zhu"> Zihang Zhu</a>, <a href="https://publications.waset.org/abstracts/search?q=Xuan%20Li"> Xuan Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Wei%20Jiang"> Wei Jiang</a>, <a href="https://publications.waset.org/abstracts/search?q=Qiurong%20Zheng"> Qiurong Zheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Hui%20Wang"> Hui Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a frequency-dependent and tunable phase shifter is proposed and numerically analyzed. The key devices are the dual-polarization binary phase shift keying modulator (DP-BPSK) and the fiber Bragg grating (FBG). The phase-frequency response of the FBG is employed to determine the frequency-dependent phase shift. The simulation results show that a linear phase shift of the recovered output microwave signal which depends on the frequency of the input RF signal is achieved. In addition, by adjusting the power of the RF signal, the full range phase shift from 0° to 360° can be realized. This structure shows the spurious free dynamic range (SFDR) of 70.90 dB·Hz<sup>2/3</sup> and 72.11 dB·Hz<sup>2/3</sup> under different RF powers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=microwave%20photonics" title="microwave photonics">microwave photonics</a>, <a href="https://publications.waset.org/abstracts/search?q=phase%20shifter" title=" phase shifter"> phase shifter</a>, <a href="https://publications.waset.org/abstracts/search?q=spurious%20free%20dynamic%20range" title=" spurious free dynamic range"> spurious free dynamic range</a>, <a href="https://publications.waset.org/abstracts/search?q=frequency-dependent" title=" frequency-dependent"> frequency-dependent</a> </p> <a href="https://publications.waset.org/abstracts/95223/frequency-dependent-and-full-range-tunable-phase-shifter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95223.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">296</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=mel%20frequency%20cepstral%20coeffiecients&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=mel%20frequency%20cepstral%20coeffiecients&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=mel%20frequency%20cepstral%20coeffiecients&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=mel%20frequency%20cepstral%20coeffiecients&page=5">5</a></li> <li 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