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Academic literature on the topic 'MFCC'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 October 2023

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Journal articles on the topic "MFCC"

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Lankala, Srinija, and Dr M. Ramana Reddy. "Design and Implementation of Energy-Efficient Floating Point MFCC Extraction Architecture for Speech Recognition Systems." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (September 30, 2022): 1217–25. http://dx.doi.org/10.22214/ijraset.2022.46807.

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Abstract: This brief presents an energy-efficient architecture to extract mel-frequency cepstrum coefficients (MFCCs) for realtime speech recognition systems. Based on the algorithmic property of MFCC feature extraction, the architecture is designed with floating-point arithmetic units to cover a wide dynamic range with a small bit-width. Moreover, various operations required in the MFCC extraction are examined to optimize operational bit-width and lookup tables needed to compute nonlinear functions, such as trigonometric and logarithmic functions. In addition, the dataflow of MFCC extraction is tailored to minimize the computation time. As a result, the energy consumption is considerably reduced compared with previous MFCC extraction systems
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Chu, Yun Yun, Wei Hua Xiong, Wei Wei Shi, and Yu Liu. "The Extraction of Differential MFCC Based on EMD." Applied Mechanics and Materials 313-314 (March 2013): 1167–70. http://dx.doi.org/10.4028/www.scientific.net/amm.313-314.1167.

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Feature extraction is the key to the object recognition. How to obtain effective, reliable characteristic parameters from the limited measured data is a question of great importance in feature extraction. This paper presents a method based on Empirical Mode Decomposition (EMD) for the extraction of Mel Frequency Cepstrum Coefficients (MFCCs) and its first order difference from original speech signals that contain four kinds of emotions such as anger, happiness, surprise and natural for emotion recognition. And the experiments compare the recognition rate of MFCC, differential MFCC (Both of them are extracted based on EMD) or their combination through using Support Vector Machine (SVM) to recognize speakers' emotional speech identity. It proves that the combination of MFCC and its first order difference has a highest recognition rate.
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Mohammed, Duraid Y., Khamis Al-Karawi, and Ahmed Aljuboori. "Robust speaker verification by combining MFCC and entrocy in noisy conditions." Bulletin of Electrical Engineering and Informatics 10, no. 4 (August 1, 2021): 2310–19. http://dx.doi.org/10.11591/eei.v10i4.2957.

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Automatic speaker recognition may achieve remarkable performance in matched training and test conditions. Conversely, results drop significantly in incompatible noisy conditions. Furthermore, feature extraction significantly affects performance. Mel-frequency cepstral coefficients MFCCs are most commonly used in this field of study. The literature has reported that the conditions for training and testing are highly correlated. Taken together, these facts support strong recommendations for using MFCC features in similar environmental conditions (train/test) for speaker recognition. However, with noise and reverberation present, MFCC performance is not reliable. To address this, we propose a new feature 'entrocy' for accurate and robust speaker recognition, which we mainly employ to support MFCC coefficients in noisy environments. Entrocy is the fourier transform of the entropy, a measure of the fluctuation of the information in sound segments over time. Entrocy features are combined with MFCCs to generate a composite feature set which is tested using the gaussian mixture model (GMM) speaker recognition method. The proposed method shows improved recognition accuracy over a range of signal-to-noise ratios.
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Eskidere, Ömer, and Ahmet Gürhanlı. "Voice Disorder Classification Based on Multitaper Mel Frequency Cepstral Coefficients Features." Computational and Mathematical Methods in Medicine 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/956249.

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The Mel Frequency Cepstral Coefficients (MFCCs) are widely used in order to extract essential information from a voice signal and became a popular feature extractor used in audio processing. However, MFCC features are usually calculated from a single window (taper) characterized by large variance. This study shows investigations on reducing variance for the classification of two different voice qualities (normal voice and disordered voice) using multitaper MFCC features. We also compare their performance by newly proposed windowing techniques and conventional single-taper technique. The results demonstrate that adapted weighted Thomson multitaper method could distinguish between normal voice and disordered voice better than the results done by the conventional single-taper (Hamming window) technique and two newly proposed windowing methods. The multitaper MFCC features may be helpful in identifying voices at risk for a real pathology that has to be proven later.
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Abdul, Zrar Khalid. "Kurdish Spoken Letter Recognition based on k-NN and SVM Model." Journal of University of Raparin 7, no. 4 (November 30, 2020): 1–12. http://dx.doi.org/10.26750/vol(7).no(4).paper1.

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Automatic recognition of spoken letters is one of the most challenging tasks in the area of speech recognition system. In this paper, different machine learning approaches are used to classify the Kurdish alphabets such as SVM and k-NN where both approaches are fed by two different features, Linear Predictive Coding (LPC) and Mel Frequency Cepstral Coefficients (MFCCs). Moreover, the features are combined together to learn the classifiers. The experiments are evaluated on the dataset that are collected by the authors as there as not standard Kurdish dataset. The dataset consists of 2720 samples as a total. The results show that the MFCC features outperforms the LPC features as the MFCCs have more relative information of vocal track. Furthermore, fusion of the features (MFCC and LPC) is not capable to improve the classification rate significantly.
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Raychaudhuri, Aryama, Rudra Narayan Sahoo, and Manaswini Behera. "Application of clayware ceramic separator modified with silica in microbial fuel cell for bioelectricity generation during rice mill wastewater treatment." Water Science and Technology 84, no. 1 (June 4, 2021): 66–76. http://dx.doi.org/10.2166/wst.2021.213.

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Abstract Ceramic separators have recently been investigated as low-cost, robust, and sustainable separators for application in microbial fuel cells (MFC). In the present study, an attempt was made to develop a low-cost MFC employing a clayware ceramic separator modified with silica. The properties of separators with varying silica content (10%–40% w/w) were evaluated in terms of oxygen and proton diffusion. The membrane containing 30% silica exhibited improved performance compared to the unmodified membrane. Two identical MFCs, fabricated using ceramic separators with 30% silica content (MFCS-30) and without silica (MFCC), were operated at hydraulic retention time of 12 h with real rice mill wastewater with a chemical oxygen demand (COD) of 3,200 ± 50 mg/L. The maximum volumetric power density of 791.72 mW/m3 and coulombic efficiency of 35.77% was obtained in MFCS-30, which was 60.4% and 48.5%, respectively, higher than that of MFCC. The maximum COD and phenol removal efficiency of 76.2% and 58.2%, respectively, were obtained in MFCS-30. MFC fabricated with modified ceramic separator demonstrated higher power generation and pollutant removal. The presence of hygroscopic silica in the ceramic separator improved its performance in terms of hydration properties and proton transport.
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Huizen, Roy Rudolf, and Florentina Tatrin Kurniati. "Feature extraction with mel scale separation method on noise audio recordings." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 2 (November 1, 2021): 815. http://dx.doi.org/10.11591/ijeecs.v24.i2.pp815-824.

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This paper focuses on improving the accuracy of noise audio recordings. High-quality audio recording, extraction using the mel frequency cepstral coefficients (MFCC) method produces high accuracy. While the low-quality is because of noise, the accuracy is low. Improved accuracy by investigating the effect of bandwidth on the mel scale. The proposed improvement uses the mel scale separation methods into two frequency channels (MFCC dual-channel). For the comparison method using the mel scale bandwidth without separation (MFCC single-channel). Feature analysis using k-mean clustering. The data uses a noise variance of up to -16 dB. Testing on the MFCC single-channel method for -16 dB noise has an accuracy of 47.5%, while the MFCC dual-channel method has an accuracy better of 76.25%. The next test used adaptive noise-canceling (ANC) to reduce noise before extraction. The result is that the MFCC single-channel method has an accuracy of 82.5% and the MFCC dual-channel method has an accuracy better of 83.75%. High-quality audio recording testing for the MFCC single-channel method has an accuracy of 92.5% and the MFCC dual-channel method has an accuracy better of 97.5%. The test results show the effect of mel scale bandwidth to increase accuracy. The MFCC dual-channel method has higher accuracy.
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Zhou, Ping, Xiao Pan Li, Jie Li, and Xin Xing Jing. "Speech Emotion Recognition Based on Mixed MFCC." Applied Mechanics and Materials 249-250 (December 2012): 1252–58. http://dx.doi.org/10.4028/www.scientific.net/amm.249-250.1252.

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Due to MFCC characteristic parameter in speech recognition has low identification accuracy when signal is intermediate, high frequency signal, this paper put forward a improved algorithm of combining MFCC, Mid-MFCC and IMFCC, using increase or decrease component method to calculate the contribution that MFCC, Mid-MFCC and IMFCC each order cepstrum component was used in speech emotion recognition, extracting several order cepstrum component with highest contribution from three characteristic parameters and forming a new characteristic parameter. The experiment results show that under the same environment new characteristic parameter has higher recognition rate than classic MFCC characteristic parameter in speech emotion recognition.
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Sharma, Samiksha, Anupam Shukla, and Pankaj Mishra. "Speech and Language Recognition using MFCC and DELTA-MFCC." International Journal of Engineering Trends and Technology 12, no. 9 (June 25, 2014): 449–52. http://dx.doi.org/10.14445/22315381/ijett-v12p286.

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G., Rupali, and S. K. Bhatia. "Analysis of MFCC and Multitaper MFCC Feature Extraction Methods." International Journal of Computer Applications 131, no. 4 (December 17, 2015): 7–10. http://dx.doi.org/10.5120/ijca2015906883.

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Dissertations / Theses on the topic "MFCC"

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Mukherjee, Rishiraj. "Speaker Recognition Using Shifted MFCC." Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/4136.

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Speaker Recognition is the art of recognizing a speaker from a given database using speech as the only input. In this thesis we will be discussing a novel approach to detect speakers. Here we will introduce the concept of shifted MFCC to add improvement over the performance from previous work which has shown quite a decent amount of accuracy of about 95% at best. We will be talking about adding different parameters which also contributed in improving the efficiency of speaker recognition. Also we will be testing our algorithm on Text dependent speech data and Text Independent speech data. Our technique was evaluated on TIDIGIT - database. In order to further increase the speaker recognition rate at lower FARs, we combined accent information added with pitch and higher order formants. The possible application areas for the work done here is in any access control entry system or now a day's a lot of smart phones, laptops, operating systems etc have Also, in homeland security applications; speaker accent will play a critical role in the evaluation of biometric systems since users will be international in nature. So incorporating accent information into the speaker recognition/verification system is a key component that our study focused on. The accent incorporation method and Shifted MFCC techniques discussed in this work can also be applied to any other speaker recognition systems.
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Tolunay, Atahan. "Text-Dependent Speaker Verification Implemented in Matlab Using MFCC and DTW." Thesis, Linköpings universitet, Informationskodning, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-60992.

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Even though speaker verification is a broad subject, the commercial and personal use implementations are rare. There are several problems that need to be solved before speaker verification can become more useful. The amount of pattern matching and feature extraction techniques is large and the decision on which ones to use is debatable. One of the main problems of speaker verification in general is the impact of noise. The very popular feature extraction technique MFCC is inherently sensitive to mismatch between training and verification conditions. MFCC is used in many speech recognition applications and is not only useful in text-dependent speaker verification. However the most reliable verification techniques are text-dependent. One of the most popular pattern matching techniques in text-dependent speaker verification is DTW. Although having limitations outside the text-dependent applications it is a reliable way of matching templates even with limited amount of training material. The signal processing techniques, MFCC and DTW are explained and discussed in detail along with a Matlab program where these techniques have been implemented. The choices made in signal processing, feature extraction and pattern matching  are determined by discussions of available studies on these topics. The results indicate that it is possible to program text-dependent speaker verification systems that are functional in clean conditions with tools like Matlab.
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Krotký, Jan. "Dekodér pro systém detekce klíčových slov." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-218176.

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The essay presents the basic characteristics of human speech recognition, describes systems for the detection of key words and further deals with the proposal of each decoder blocks divided into three chapters. The first one describes the operations that are performed before the signal distribution of the framework and the segmentation. The second chapter describes the calculation of short-term energy, the number of zero passes and self-correlative, prediction and Mel-frequency cepstral coefficients. The third chapter, which describes the design of the block decoder, describes the method of dynamic time destruction and the method based on hidden Markov model. The final part of the essay describes decoders working with a speech and a proposal for a simple decoder working with isolated words, which was based issued and tested based on the preceding chapters.
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Mubarak, Omer Mohsin Electrical Engineering &amp Telecommunications Faculty of Engineering UNSW. "Speech and music discrimination using short-time features." Awarded by:University of New South Wales. Electrical Engineering & Telecommunications, 2006. http://handle.unsw.edu.au/1959.4/31954.

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This thesis addresses the problem of classifying an audio stream as either speech or music, an issue which is beginning to receive increasing attention due to its wide range of applications. Various techniques have been presented in last decade to discriminate between speech and music. However, their accuracy is still not sufficient since music can refer to a very broad class of signals due to the large number of musical instruments found in audio data. Performance can also be further compromised in noisy conditions, which are unavoidable in some practical situations. This thesis presents an analysis of feature extraction techniques and classifiers currently being used, followed by the proposal and evaluation of new features for improved classification. These include two novel cepstral features, delta cepstral energy and power spectrum deviation, along with amplitude and frequency modulation features. The modified group delay feature, initially proposed for speech recognition, is also investigated for speech and music discrimination. Experiments were performed using different sets of features, compared among themselves and with conventional MFCCs using error rate criteria and Detection Error Trade-off curves. It is shown that the proposed cepstral and modulation features result in an increase in the accuracy of the conventional MFCC based system. However, the modified group delay feature which has been shown to improve accuracy for speech classification problems, does not contribute much to the problem of speech and music discrimination. Among the ones presented here the optimum feature configuration, both modulation features with MFCC, resulted in overall error rate of 6.57% as compared to 7.43% for MFCC alone.
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Pan, Linlin. "Research and simulation on speech recognition by Matlab." Thesis, Högskolan i Gävle, Avdelningen för elektronik, matematik och naturvetenskap, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-16950.

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With the development of multimedia technology, speech recognition technology has increasingly become a hotspot of research in recent years. It has a wide range of applications, which deals with recognizing the identity of the speakers that can be classified into speech identification and speech verification according to decision modes.The main work of this thesis is to study and research the techniques, algorithms of speech recognition, thus to create a feasible system to simulate the speech recognition. The research work and achievements are as following: First: The author has done a lot of investigation in the field of speech recognition with the adequate research and study. There are many algorithms about speech recognition, to sum up, the algorithms can divided into two categories, one of them is the direct speech recognition, which means the method can recognize the words directly, and another prefer the second method that recognition based on the training model. Second: find a useable and reasonable algorithm and make research about this algorithm. Besides, the author has studied algorithms, which are used to extract the word's characteristic parameters based on MFCC(Mel frequency Cepstrum Coefficients) , and training the Characteristic parameters based on the GMM(Gaussian mixture mode) . Third: The author has used the MATLAB software and written a program to implement the speech recognition algorithm and also used the speech process toolbox in this program. Generally speaking, whole system includes the module of the signal process, MFCC characteristic parameter and GMM training. Forth: Simulation and analysis the results. The MATLAB system will read the wav file, play it first, and then calculate the characteristic parameters automatically. All content of the speech signal have been distinguished in the last step. In this paper, the author has recorded speech from different people to test the systems and the simulation results shown that when the testing environment is quiet enough and the speaker is the same person to record for 20 times, the performance of the algorithm is approach to 100% for pair of words in different and same syllable. But the result will be influenced when the testing signal is surrounded with certain noise level. The simulation system won’t work with a good output, when the speaker is not the same one for recording both reference and testing signal.
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SIQUEIRA, JAN KRUEGER. "CONTINUOUS SPEECH RECOGNITION WITH MFCC, SSCH AND PNCC FEATURES, WAVELET DENOISING AND NEURAL NETWORKS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2011. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=19143@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
Um dos maiores desafios na área de reconhecimento de voz contínua é desenvolver sistemas robustos ao ruído aditivo. Para isso, este trabalho analisa e testa três técnicas. A primeira delas é a extração de atributos do sinal de voz usando os métodos MFCC, SSCH e PNCC. A segunda é a remoção de ruído do sinal de voz via wavelet denoising. A terceira e última é uma proposta original batizada de feature denoising, que busca melhorar os atributos extraídos usando um conjunto de redes neurais. Embora algumas dessas técnicas já sejam conhecidas na literatura, a combinação entre elas trouxe vários resultados interessantes e inéditos. Inclusive, nota-se que o melhor desempenho vem da união de PNCC com feature denoising.
One of the biggest challenges on the continuous speech recognition field is to develop systems that are robust to additive noise. To do so, this work analyses and tests three techniques. The first one extracts features from the voice signal using the MFCC, SSCH and PNCC methods. The second one removes noise from the voice signal through wavelet denoising. The third one is an original one, called feature denoising, that seeks to improve the extracted features using a set of neural networks. Although some of these techniques are already known in the literature, the combination of them brings many interesting and new results. In fact, it is noticed that the best performance comes from the union of PNCC and feature denoising.
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Dobrovolskis, Martynas. "Šnekos atpažinimas." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2005. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2005~D_20050614_154005-58155.

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Voice recognition technologies appeared in the period of general device miniaturization, when all technologies were commonly integrated into one lust. There is no space for buttons and displays anymore. To have a good system of Lithuanian language recognition, a number of throughout researches must be implemented. Only after selecting the most efficient speech recognition scheme, we can proceed to the development of software adapted to the contemporary time. The aim of this paper is to determine, how efficient speech recognition is possible using neuron networks. MFCC and LPC coefficients were chosen as the parameters characterizing the phonemes. The paper attempts at the determination of the coefficients, which lead to the most efficient recognition of phonemes. For testing, programs PRAAT and MatLab were used. After implementing a number of phoneme recognition experiments in the research work, the results were obtained, which lead to the following conclusions: 1. In case of using neuron network for the recognition of isolated sounds and characterizing the phonemes by MFCC or LPC coefficients, the possibility of recognition does not exceed 90 per cent. It is not enough for quality recognition of Lithuanian speech. 2. In case of using MFCC coefficients, separate phonemes are recognized better than using LPC coefficients. The difference is about 15 per cent. 3. The advantage of LPC coefficients in comparison with MFCC is the curve of recognition possibility, which is more even... [to full text]
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Julien, Eric. "Alignement du chant par rapport à une référence audio en temps réel." Mémoire, Université de Sherbrooke, 2013. http://hdl.handle.net/11143/6184.

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Dans l'optique de créer un système de karaoké qui modifie une interprétation chantée à capella en temps réel, il est nécessaire de pouvoir localiser l'interprète par rapport à une référence afin de pouvoir déterminer quelle serait la cible d'un algorithme de modification de la voix. Pour qu'un tel système fonctionne bien, il est nécessaire que l'algorithme d'alignement exploite au maximum les spécificités de la voix, qu'il utilise l'information liée au texte prononcé plutôt qu'aux aspects artistiques du chant, qu'il soit à temps réel et qu'il offr la plus faible latence possible. Afin d'atteindre ces objectifs, un système d'alignement basé sur le Dynamic Time Warping (DTW) a été développé. Une adaptation temps réel simple de l'algorithme ordinaire de la DTW qui permet d'atteindre les objectifs énumérés est proposée et comparée à d'autres approches répertoriées dans la littérature. Cette adaptation a permis d'obtenir de meilleurs résultats que les autres techniques testées. Une étude comparative de trois types d'analyses spectrales couramment utilisées dans des systèmes de reconnaissance automatique de la voix a été réalisée, dans le cadre spécifique d'un algorithme d'alignement de la voix chantée. Les coefficients évalués sont les Mel-frquency Cepstrum Coefficients (MFCC), les Warped Discrete Cosine Transform Coefficients (WDCTC) et les coefficients de l'analyse Perceptual Linear Prediction (PLP). Les résultats obtenus indiquent une meilleure performance pour l'analyse PLP. L'utilisation d'une fonction de transformation linéaire par morceaux, appliquée aux matrices de coûts instantanés obtenues, permet de rendre l'alignement le plus facilement distinguable dans les matrices de coûts cumulés calculées. Les paramètres de la fonction de transformation peuvent être obtenus par l'optimisation en boucle fermée par recherche directe par motif. Une fonction-objectif permettant d'éviter les discontinuités de l'écart quadratique moyen sur l'alignement est développée. Plusieurs matrices de coûts peuvent être combinées entre elles en effectuant une somme pondérée des matrices de coûts instantanées transformées de chacun des paramètres considérés. La pondération est également obtenue par optimisation. Plusieurs assemblages sont comparés : les meilleurs résultats sont obtenus avec une combinaison de l'analyse PLP et du niveau d'énergie et des dérivées de ceux-ci. L'écart moyen sur l'alignement de référence est de l'ordre de 50 ms, avec un écart-type d'environ 75 ms pour les séquences testées. Des perspectives permettant d'améliorer la convergence de l'algorithme pour les paires de séquences audio difficiles à aligner, d'obtenir de meilleures matrices de coûts en utilisant d'autres contraintes locales, en considérant l'intégration de nouveaux paramètres tels le pitch ou en utilisant une base de données de voix chantée segmentée pour optimiser une mesure de distance sont données.
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Martins, Ana Caroline Vasconcelos. "GluA2 - Glutamatergic Receptor Study: A Molecular Approach." reponame:Repositório Institucional da UFC, 2017. http://www.repositorio.ufc.br/handle/riufc/28258.

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Glutamate receptors are the mediators of most excitatory neurotransmission processes in the central nervous system, acting as prominent targets for the treatment of several neurological disorders such as Epilepsy, Amyotrophic Lateral Sclerosis, Parkinson’s disease and Alzheimer’s disease. Hence an improved understanding of how glutamate and other ligands interact with the binding domain, of these receptors, can bring relevant insights to the development of new ligands. Therefore, this work aims to study the GluA2–ligand interaction using the structure of GluA2 co-crystallized with the ligands glutamate, AMPA, kainate and DNQX applying a method based on the Density Functional Theory combined with the molecular fractionation with conjugate caps scheme. To address that the dielectric constant of the GluA2 receptor is not homogeneous, a novel molecular approach was proposed and it was applied to study the interaction between the GluA2 and the ligands glutamate, AMPA, kainate and DNQX. The results obtained, considering the inhomogeneous model, were compared with those obtained using an uniform dielectric function for the GluA2 receptor and with data published in the literature establishing a more detailed description of the relevant amino acid residues for the protein-ligand binding interaction. Molecular dynamics studies and protein DFT calculations usually consider a fixed value for the protein dielectric function. In this work when ε = 1 is considered, many amino acid residues seem important, but when the dielectric constant shield was considered, they lost their relevance. The results for the GluA2-ligand total interaction energy and the D1-ligand and D2-ligand total interaction energy also shed some light on the differentiation between full and partial agonists, and between agonists and antagonists. Additionally, the results allow a hypothesis on the correlation between the Glu705-ligand interaction energy and the ligand action, paving the way for the use of the inhomogeneous dielectric function to study glutamate receptors and other protein-ligand systems. Finally, the results also suggests that for different ligands, different homogeneous dielectric constant will be able to well represent the system GluA2-ligand, making it necessary the previous analyses with the inhomogeneous dielectric constant approach.
Os receptores de glutamato são os mediadores da maioria dos processos de neurotransmissão excitatória no sistema nervoso central, atuando como alvos proeminentes para o tratamento de vários distúrbios neurológicos, como Epilepsia, Esclerose Lateral Amiotrófica, Doença de Parkinson e Doença de Alzheimer. Assim, uma compreensão aprimorada de como o glutamato e outros ligantes interagem com o domínio de interação, desses receptores, pode trazer informações relevantes para o desenvolvimento de novos ligantes. Portanto, este trabalho teve por objetivo estudar a interação GluA2-ligante utilizando a estrutura de GluA2 co-cristalizada com os ligantes Glutamato, AMPA, Cainato e DNQX utilizando método baseado na Teoria do Funcional da Densidade combinado com o esquema de fracionamento molecular com capas conjugadas. Para abordar que a constante dielétrica do receptor GluA2 não é homogênea, foi proposta uma nova abordagem molecular, que foi aplicada para estudar a interação entre a GluA2 e os ligantes Glutamato, AMPA, Cainato e DNQX. Os resultados obtidos, considerando o modelo não-homogêneo, foram comparados com aqueles obtidos usando uma função dielétrica uniforme para o receptor GluA2 e com dados publicados na literatura, estabelecendo uma descrição mais detalhada dos resíduos de aminoácido mais relevantes para a interação proteína-ligante. Estudos de dinâmica molecular e cálculos DFT de sistemas proteicos normalmente consideram um valor fixo para a função dielétrica proteica. Nesse trabalho quando ε = 1 é considerado, muitos resíduos de aminoácido parecem relevantes, mas quando a blindagem da constante dielétrica foi considerada, eles perderam sua relevância. Os resultados apresentados para a energia de interação total GluA2-ligante e a energia de interação total D1-ligante e D2-ligante contribuiu com a diferenciação entre agonistas totais e agonistas parciais e entre agonistas e antagonistas. Além disso, os resultados permitem que seja feita hipótese sobre a correlação entre a energia de interação Glu705-ligante e a ação do ligante, abrindo caminho para o uso da função dielétrica não-homogênea para estudar receptores de glutamato e outros sistemas proteína-ligante. Por fim, os resultados também sugerem que para diferentes ligantes, diferentes constantes dielétricas homogêneas serão capazes de representar bem o sistema GluA2-ligante, tornando necessária a análise prévia com a abordagem da constante dielétrica não-homogênea.
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SILVA, HARRY ARNOLD ANACLETO. "INDEPENDENT TEXT ROBUST SPEAKER RECOGNITION IN THE PRESENCE OF NOISE USING PAC-MFCC AND SUB BAND CLASSIFIERS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2011. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=18212@1.

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Abstract:
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
O presente trabalho é proposto o atributo PAC-MFCC operando com Classificadores em Sub-Bandas para a tarefa de identificação de locutor independente do texto em ruído. O sistema proposto é comparado com os atributos MFCC (Coeficientes Cepestrais de Frequência Mel), PAC- MFCC (Fase Autocorrelação-MFCC ) sem uso de classificadores em sub-bandas, SSCH(Histogramas de Centróides de Sub-Bandas Espectrais) e TECC (Coeficientes Cepestrais da Energia Teager). Nesta tarefa de reconhecimento, utilizou-se a base TIMIT a qual é composta de 630 locutores onde cada um deles falam 10 frases de aproximadamente 3 segundos cada frase, das quais 8 frases foram utilizadas para treinamento e 2 para teste, obtendo-se um total de 1260 locuções para o reconhecimento. Investigou-se o desempenho dos diversos sistemas utilizando diferentes tipos de ruídos da base Noisex 92 com diferentes relação sinal ruído. Verificou-se que a taxa de acerto da técnica PAC-MFCC com classificador em Sub-Bandas apresenta o melhor desempenho em comparação com as outras técnicas quando se tem uma relação sinal ruído menor que 10dB.
In this work is proposed the use of the PAC-MFCC feature with Sub-band Classifiers for the task of text-independent speaker identification in noise. The proposed scheme is compared with the features MFCC (Mel-Frequency Cepstral Coefficients ), PAC-MFCC (Phase Autocorrelation MFCC) without subband classifiers, SSCH (Subband Spectral Centroid Histograms) and TECC (Teager Energy Cepstrum Coefficients). In this recognition task, we used the TIMIT database which consists of 630 speakers, where every one of them speak 10 utterances of 3 seconds each one approximately, of which eight utterance were used for training and two for testing, thus obtaining a total of 1260 test utterance for the recognition. We investigated the performance of these techniques using differents types of noise from the base Noisex 92 with different signal to noise ratios. It was found that the accuracy rate of the PAC-MFCC feature with Sub-band Classifiers performs better in comparison with other techniques at a lower signal noise(less than 10dB).
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Books on the topic "MFCC"

1

Shen ru qian chu MFC: Dissecting MFC. 2nd ed. Wuhan: Hua zhong ke ji da xue chu ban she, 2001.

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MFC programming. Reading, Mass: Addison-Wesley, 1997.

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Intermediate MFC. Upper Saddle River, NJ: Prentice Hall PTR, 1998.

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Crockett, Frank. MFC Developer's workshop. Redmond, Wash: Microsoft Press, 1997.

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MFC black book. Albany, NY: Coriolis Group Books, 1998.

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E, Robichaux Paul, ed. Using MFC and ATL. Indianapolis, IN: QUE, 1997.

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Japan. Keizai Sangyōshō. Sangyō Gijutsu Kankyōkyoku. Kankyō Chōwa Sangyō Suishinshitsu. Materiaru furō kosuto kaikei (MFCA) dōnyū jireishū. Tōkyō: Keizai Sangyōshō Sangyō Gijutsu Kankyōkyoku Kankyō Seisakuka Kankyō Chōwa Sangyō Suishinshitsu, 2008.

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Programming Windows 95 with MFC. Redmond, Wash: Microsoft Press, 1996.

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Learn the MFC C++ classes. Plano, Tex: Wordware Pub., 1997.

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Schildt, Herbert. MFC programming from the ground up. 2nd ed. Berkeley, Calif: Osborne/McGraw-Hill, 1998.

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Book chapters on the topic "MFCC"

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Yan, Qin, Zhengjuan Zhou, and Shan Li. "Chinese Accents Identification with Modified MFCC." In Advances in Intelligent and Soft Computing, 659–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27334-6_77.

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Liu, Jinfeng, Tong Zhu, Xiao He, and John Z. H. Zhang. "MFCC-Based Fragmentation Methods for Biomolecules." In Fragmentation, 323–48. Chichester, UK: John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781119129271.ch11.

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Gonzalez, Ruben. "Better Than MFCC Audio Classification Features." In The Era of Interactive Media, 291–301. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3501-3_24.

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Gulhane, Sushen R., D. Shirbahadurkar Suresh, and S. Badhe Sanjay. "Identification of Musical Instruments Using MFCC Features." In New Trends in Computational Vision and Bio-inspired Computing, 957–68. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41862-5_97.

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Sethi, Nandini, and Dinesh Kumar Prajapati. "Text-Independent Voice Authentication System Using MFCC Features." In Advances in Intelligent Systems and Computing, 567–77. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5113-0_45.

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Singh, Vrijendra, and Narendra Meena. "Engine Fault Diagnosis using DTW, MFCC and FFT." In Proceedings of the First International Conference on Intelligent Human Computer Interaction, 83–94. New Delhi: Springer India, 2009. http://dx.doi.org/10.1007/978-81-8489-203-1_6.

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Umarani, S. D., R. S. D. Wahidabanu, and P. Raviram. "Isolated Word Recognition Using Enhanced MFCC and IIFs." In Advances in Intelligent Systems and Computing, 273–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35314-7_32.

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Li, Fuhai, Jinwen Ma, and Dezhi Huang. "MFCC and SVM Based Recognition of Chinese Vowels." In Computational Intelligence and Security, 812–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11596981_118.

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Ding, Kai, Shoujun Zheng, Xiaogang Qi, Shan Huang, and Haoting Liu. "Acoustic Target Recognition Based on MFCC and SVM." In Man-Machine-Environment System Engineering, 418–23. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4786-5_58.

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Wang, Kai, and Kang Chen. "Classification of Heart Sounds Using MFCC and CNN." In Intelligent Computing Theories and Application, 745–56. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-84529-2_62.

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Conference papers on the topic "MFCC"

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Bansal, Priyanka, Syed Akhtar Imam, and Roma Bharti. "Speaker recognition using MFCC, shifted MFCC with vector quantization and fuzzy." In 2015 International Conference on Soft Computing Techniques and Implementations (ICSCTI). IEEE, 2015. http://dx.doi.org/10.1109/icscti.2015.7489535.

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Paseddula, Chandrasekhar, and Suryakanth V. Gangashetty. "DNN based Acoustic Scene Classification using Score Fusion of MFCC and Inverse MFCC." In 2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS). IEEE, 2018. http://dx.doi.org/10.1109/iciinfs.2018.8721379.

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Nagawade, Monica S., and Varsha R. Ratnaparkhe. "Musical instrument identification using MFCC." In 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). IEEE, 2017. http://dx.doi.org/10.1109/rteict.2017.8256990.

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Jhawar, Gunjan, Prajacta Nagraj, and P. Mahalakshmi. "Speech disorder recognition using MFCC." In 2016 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2016. http://dx.doi.org/10.1109/iccsp.2016.7754132.

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Vijayan, Amritha, Bipil Mary Mathai, Karthik Valsalan, Riyanka Raji Johnson, Lani Rachel Mathew, and K. Gopakumar. "Throat microphone speech recognition using mfcc." In 2017 International Conference on Networks & Advances in Computational Technologies (NetACT). IEEE, 2017. http://dx.doi.org/10.1109/netact.2017.8076802.

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Wu, Yi, Qi Wang, and Ruolun Liu. "Music Instrument Classification using Nontonal MFCC." In 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/fmsmt-17.2017.88.

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Chatterjee, Saikat, and W. Bastiaan Kleijn. "Auditory model based modified MFCC features." In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2010. http://dx.doi.org/10.1109/icassp.2010.5495557.

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Suwannakhun, Sirimonpak, and Thaweesak Yingthawornsuk. "Characterizing Depressive Related Speech with MFCC." In 2019 14th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP). IEEE, 2019. http://dx.doi.org/10.1109/isai-nlp48611.2019.9045499.

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El Badlaoui, Othmane, and Ahmed Hammouch. "Phonocardiogram classification based on MFCC extraction." In 2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA). IEEE, 2017. http://dx.doi.org/10.1109/civemsa.2017.7995329.

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Kou, Haofeng, Weijia Shang, Ian Lane, and Jike Chong. "Optimized MFCC feature extraction on GPU." In ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6639046.

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Reports on the topic "MFCC"

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Jones, Robert, Molly Creagar, Michael Musty, Randall Reynolds, Scott Slone, and Robyn Barbato. A 𝘬-means analysis of the voltage response of a soil-based microbial fuel cell to an injected military-relevant compound (urea). Engineer Research and Development Center (U.S.), November 2022. http://dx.doi.org/10.21079/11681/45940.

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Biotechnology offers new ways to use biological processes as environmental sensors. For example, in soil microbial fuel cells (MFCs), soil electro-genic microorganisms are recruited to electrodes embedded in soil and produce electricity (measured by voltage) through the breakdown of substrate. Because the voltage produced by the electrogenic microbes is a function of their environment, we hypothesize that the voltage may change in a characteristic manner given environmental disturbances, such as the contamination by exogenous material, in a way that can be modelled and serve as a diagnostic. In this study, we aimed to statistically analyze voltage from soil MFCs injected with urea as a proxy for gross contamination. Specifically, we used 𝘬-means clustering to discern between voltage output before and after the injection of urea. Our results showed that the 𝘬-means algorithm recognized 4–6 distinctive voltage regions, defining unique periods of the MFC voltage that clearly identify pre- and postinjection and other phases of the MFC lifecycle. This demonstrates that 𝘬-means can identify voltage patterns temporally, which could be further improve the sensing capabilities of MFCs by identifying specific regions of dissimilarity in voltage, indicating changes in the environment.
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Rossi, Ruggero, David Jones, Jaewook Myung, Emily Zikmund, Wulin Yang, Yolanda Alvarez Gallego, Deepak Pant, et al. Evaluating a multi-panel air cathode through electrochemical and biotic tests. Engineer Research and Development Center (U.S.), December 2022. http://dx.doi.org/10.21079/11681/46320.

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To scale up microbial fuel cells (MFCs), larger cathodes need to be developed that can use air directly, rather than dissolved oxygen, and have good electrochemical performance. A new type of cathode design was examined here that uses a “window-pane” approach with fifteen smaller cathodes welded to a single conductive metal sheet to maintain good electrical conductivity across the cathode with an increase in total area. Abiotic electrochemical tests were conducted to evaluate the impact of the cathode size (exposed areas of 7 cm², 33 cm², and 6200 cm²) on performance for all cathodes having the same active catalyst material. Increasing the size of the exposed area of the electrodes to the electrolyte from 7 cm² to 33 cm² (a single cathode panel) decreased the cathode potential by 5%, and a further increase in size to 6200 cm² using the multi-panel cathode reduced the electrode potential by 55% (at 0.6 A m⁻²), in a 50 mM phosphate buffer solution (PBS). In 85 L MFC tests with the largest cathode using wastewater as a fuel, the maximum power density based on polarization data was 0.083 ± 0.006Wm⁻² using 22 brush anodes to fully cover the cathode, and 0.061 ± 0.003Wm⁻² with 8 brush anodes (40% of cathode projected area) compared to 0.304 ± 0.009Wm⁻² obtained in the 28 mL MFC. Recovering power from large MFCs will therefore be challenging, but several approaches identified in this study can be pursued to maintain performance when increasing the size of the electrodes.
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Michael Cannon, Terry Barney, Gary Cook, Jr George Danklefsen, Paul Fairbourn, Susan Gihring, and Lisa Stearns. MFC Communications Infrastructure Study. Office of Scientific and Technical Information (OSTI), January 2012. http://dx.doi.org/10.2172/1035903.

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Medam, Anudeep, Michael Stadler, Abhishek Banerjee, Muhammad nmn Usman, Ning Kang, Adib Nasle, Kelsey Fahy, and Zack Pecenak. Summary Report for the Microgrid Fast Charging Station (MFCS) Design Platform Project. Office of Scientific and Technical Information (OSTI), July 2021. http://dx.doi.org/10.2172/1813548.

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Mickelsen, Zand. Project Closeout Report for the MFC Firewater Replacement Project. Office of Scientific and Technical Information (OSTI), June 2016. http://dx.doi.org/10.2172/1466677.

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Kerry L. Nisson. Permanent Closure of MFC Biodiesel Underground Storage Tank 99ANL00013. Office of Scientific and Technical Information (OSTI), October 2012. http://dx.doi.org/10.2172/1060996.

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Guan, Haiying, Andrew Delgado, Yooyoung Lee, Amy Yates, Daniel Zhou, Timothée Kheyrkhah, and Jonathan G. Fiscus. User Guide for NIST Media Forensic Challenge (MFC) Datasets. National Institute of Standards and Technology, July 2021. http://dx.doi.org/10.6028/nist.ir.8377.

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Aydogdu, Ali, Jaime Hernandez-Lasheras, Carolina Amadio, Baptiste Mourre, Gianpiero Cossarini, and Jenny Pistola. Design of the glider assimilation experiments. EuroSea, 2021. http://dx.doi.org/10.3289/eurosea_d4.2.

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Aydogdu, Ali. Design of the glider assimilation experiments. EuroSea, 2023. http://dx.doi.org/10.3289/eurosea_d4.2_v2.

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Steele, William F. Conceptual Design Report for the Materials and Fuels Complex (MFC) Research Collaboration Building (RCB). Office of Scientific and Technical Information (OSTI), January 2017. http://dx.doi.org/10.2172/1485427.

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