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Relevant bibliographies by topics / Mel-Frequency Cepstral coefficients

Academic literature on the topic 'Mel-Frequency Cepstral coefficients'

Author: Grafiati

Published: 4 June 2021

Last updated: 31 May 2022

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Journal articles on the topic "Mel-Frequency Cepstral coefficients"

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Sato, Nobuo, and Yasunari Obuchi. "Emotion Recognition using Mel-Frequency Cepstral Coefficients." Journal of Natural Language Processing 14, no. 4 (2007): 83–96. http://dx.doi.org/10.5715/jnlp.14.4_83.

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Hashad, F. G., T. M. Halim, S. M. Diab, B. M. Sallam, and F. E. Abd El-Samie. "Fingerprint recognition using mel-frequency cepstral coefficients." Pattern Recognition and Image Analysis 20, no. 3 (September 2010): 360–69. http://dx.doi.org/10.1134/s1054661810030120.

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Park, Won Gyeong, Young Bae Lim, Dong Woo Kim, Ho Kyoung Lee, and Sdeongwon Cho. "Prediction Method of Electrical Abnormal States Using Simplified Mel-Frequency Cepstral Coefficients." Journal of Korean Institute of Intelligent Systems 28, no. 5 (October 31, 2018): 514–22. http://dx.doi.org/10.5391/jkiis.2018.28.5.514.

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INDRAWATY, YOULLIA, IRMA AMELIA DEWI, and RIZKI LUKMAN. "Ekstraksi Ciri Pelafalan Huruf Hijaiyyah Dengan Metode Mel-Frequency Cepstral Coefficients." MIND Journal 4, no. 1 (June 1, 2019): 49–64. http://dx.doi.org/10.26760/mindjournal.v4i1.49-64.

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Huruf hijaiyyah merupakan huruf penyusun ayat dalam Al Qur’an. Setiap hurufhijaiyyah memiliki karakteristik pelafalan yang berbeda. Tetapi dalam praktiknya,ketika membaca huruf hijaiyyah terkadang tidak memperhatikan kaidah bacaanmakhorijul huruf. Makhrorijul huruf adalah cara melafalkan atau tempatkeluarnya huruf hijaiyyah. Dengan adanya teknologi pengenalan suara, dalammelafalkan huruf hijaiyyah dapat dilihat perbedaannya secara kuantitatif melaluisistem. Terdapat dua tahapan agar suara dapat dikenali, dengan terlebih dahulumelakukan ekstraksi sinyal suara selanjutnya melakukan identifikasi suara ataubacaan. MFCC (Mel Frequency Cepstral Coefficients) merupakan sebuah metodeuntuk melakukan ektraksi ciri yang menghasilkan nilai cepstral dari sinyal suara.Penelitian ini bertujuan untuk mengetahui nilai cepstral pada setiap hurufhijaiyyah. Hasil pengujian yang telah dilakukan, setiap huruf hijaiyyah memilikinilai cepstral yang berbeda.

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ARORA, SHRUTI, SUSHMA JAIN, and INDERVEER CHANA. "A FUSION FRAMEWORK BASED ON CEPSTRAL DOMAIN FEATURES FROM PHONOCARDIOGRAM TO PREDICT HEART HEALTH STATUS." Journal of Mechanics in Medicine and Biology 21, no. 04 (April 22, 2021): 2150034. http://dx.doi.org/10.1142/s0219519421500342.

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A great increase in the number of cardiovascular cases has been a cause of serious concern for the medical experts all over the world today. In order to achieve valuable risk stratification for patients, early prediction of heart health can benefit specialists to make effective decisions. Heart sound signals help to know about the condition of heart of a patient. Motivated by the success of cepstral features in speech signal classification, authors have used here three different cepstral features, viz. Mel-frequency cepstral coefficients (MFCCs), gammatone frequency cepstral coefficients (GFCCs), and Mel-spectrogram for classifying phonocardiogram into normal and abnormal. Existing research has explored only MFCCs and Mel-feature set extensively for classifying the phonocardiogram. However, in this work, the authors have used a fusion of GFCCs with MFCCs and Mel-spectrogram, and achieved a better accuracy score of 0.96 with sensitivity and specificity scores as 0.91 and 0.98, respectively. The proposed model has been validated on the publicly available benchmark dataset PhysioNet 2016.

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Sheu, Jia-Shing, and Ching-Wen Chen. "Voice Recognition and Marking Using Mel-frequency Cepstral Coefficients." Sensors and Materials 32, no. 10 (October 9, 2020): 3209. http://dx.doi.org/10.18494/sam.2020.2860.

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Koolagudi, Shashidhar G., Deepika Rastogi, and K. Sreenivasa Rao. "Identification of Language using Mel-Frequency Cepstral Coefficients (MFCC)." Procedia Engineering 38 (2012): 3391–98. http://dx.doi.org/10.1016/j.proeng.2012.06.392.

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Saldanha, Jennifer C., T. Ananthakrishna, and Rohan Pinto. "Vocal Fold Pathology Assessment Using Mel-Frequency Cepstral Coefficients and Linear Predictive Cepstral Coefficients Features." Journal of Medical Imaging and Health Informatics 4, no. 2 (April 1, 2014): 168–73. http://dx.doi.org/10.1166/jmihi.2014.1253.

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Pedalanka, P. S. Subhashini, M. SatyaSai Ram, and Duggirala Sreenivasa Rao. "Mel Frequency Cepstral Coefficients based Bacterial Foraging Optimization with DNN-RBF for Speaker Recognition." Indian Journal of Science and Technology 14, no. 41 (November 3, 2021): 3082–92. http://dx.doi.org/10.17485/ijst/v14i41.1858.

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Fahmy, Maged M. M. "Palmprint recognition based on Mel frequency Cepstral coefficients feature extraction." Ain Shams Engineering Journal 1, no. 1 (September 2010): 39–47. http://dx.doi.org/10.1016/j.asej.2010.09.005.

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Dissertations / Theses on the topic "Mel-Frequency Cepstral coefficients"

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Darch, Jonathan J. A. "Robust acoustic speech feature prediction from Mel frequency cepstral coefficients." Thesis, University of East Anglia, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.445206.

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Edman, Sebastian. "Radar target classification using Support Vector Machines and Mel Frequency Cepstral Coefficients." Thesis, KTH, Optimeringslära och systemteori, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-214794.

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In radar applications, there are often times when one does not only want to know that there is a target that reflecting the out sent signals but also what kind of target that reflecting these signals. This project investigates the possibilities to from raw radar data transform reflected signals and take use of human perception, in particular our hearing, and by a machine learning approach where patterns and characteristics in data are used to answer the earlier mentioned question. More specific the investigation treats two kinds of targets that are fairly comparable namely smaller Unmanned Aerial Vehicles (UAV) and Birds. By extracting complex valued radar video so called I/Q data generated by these targets using signal processing techniques and transform this data to a real signals and after this transform the signals to audible signals. A feature set commonly used in speech recognition namely Mel Frequency Cepstral Coefficients are used two describe these signals together with two Support Vector Machine classification models. The two models where tested with an independent test set and the linear model achieved a overall prediction accuracy 93.33 %. Individually the prediction resulted in 93.33 % correct classification on the UAV and 93.33 % on the birds. Secondly a radial basis model with a overall prediction accuracy of 98.33 % where achieved. Individually the prediction resulted in 100% correct classification on the UAV and 96.76 % on the birds. The project is partly done in collaboration with J. Clemedson [2] where the focus is, as mentioned earlier, to transform the signals to audible signals.
I radar applikationer räcker det ibland inte med att veta att systemet observerat ett mål när en reflekted signal dekekteras, det är ofta också utav stort intresse att veta vilket typ av föremål som signalen reflekterades mot. Detta projekt undersöker möjligheterna att utifrån rå radardata transformera de reflekterade signalerna och använda sina mänskliga sinnen, mer specifikt våran hörsel, för att skilja på olika mål och också genom en maskininlärnings approach där med hjälp av mönster och karaktärsdrag för dessa signaler används för att besvara frågeställningen. Mer ingående avgränsas denna undersökning till två typer av mål, mindre obemannade flygande farkoster (UAV) och fåglar. Genom att extrahera komplexvärd radar video även känt som I/Q data från tidigare nämnda typer av mål via signalbehandlingsmetoder transformera denna data till reella signaler, därefter transformeras dessa signaler till hörbara signaler. För att klassificera dessa typer av signaler används typiska särdrag som också används inom taligenkänning, nämligen, Mel Frequency Cepstral Coefficients tillsammans med två modeller av en Support Vector Machine klassificerings metod. Med den linjära modellen uppnåddes en prediktions noggrannhet på 93.33%. Individuellt var noggrannheten 93.33 % korrekt klassificering utav UAV:n och 93.33 % på fåglar. Med radial bas modellen uppnåddes en prediktions noggrannhet på 98.33%. Individuellt var noggrannheten 100 % korrekt klassificering utav UAV:n och 96.76% på fåglar. Projektet är delvis utfört med J. Clemedson [2] vars fokus är att, som tidigare nämnt, transformera dessa signaler till hörbara signaler.

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Yang, Chenguang. "Security in Voice Authentication." Digital WPI, 2014. https://digitalcommons.wpi.edu/etd-dissertations/79.

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We evaluate the security of human voice password databases from an information theoretical point of view. More specifically, we provide a theoretical estimation on the amount of entropy in human voice when processed using the conventional GMM-UBM technologies and the MFCCs as the acoustic features. The theoretical estimation gives rise to a methodology for analyzing the security level in a corpus of human voice. That is, given a database containing speech signals, we provide a method for estimating the relative entropy (Kullback-Leibler divergence) of the database thereby establishing the security level of the speaker verification system. To demonstrate this, we analyze the YOHO database, a corpus of voice samples collected from 138 speakers and show that the amount of entropy extracted is less than 14-bits. We also present a practical attack that succeeds in impersonating the voice of any speaker within the corpus with a 98% success probability with as little as 9 trials. The attack will still succeed with a rate of 62.50% if 4 attempts are permitted. Further, based on the same attack rationale, we mount an attack on the ALIZE speaker verification system. We show through experimentation that the attacker can impersonate any user in the database of 69 people with about 25% success rate with only 5 trials. The success rate can achieve more than 50% by increasing the allowed authentication attempts to 20. Finally, when the practical attack is cast in terms of an entropy metric, we find that the theoretical entropy estimate almost perfectly predicts the success rate of the practical attack, giving further credence to the theoretical model and the associated entropy estimation technique.

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Wu, Qiming. "A robust audio-based symbol recognition system using machine learning techniques." University of the Western Cape, 2020. http://hdl.handle.net/11394/7614.

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Masters of Science
This research investigates the creation of an audio-shape recognition system that is able to interpret a user’s drawn audio shapes—fundamental shapes, digits and/or letters— on a given surface such as a table-top using a generic stylus such as the back of a pen. The system aims to make use of one, two or three Piezo microphones, as required, to capture the sound of the audio gestures, and a combination of the Mel-Frequency Cepstral Coeﬃcients (MFCC) feature descriptor and Support Vector Machines (SVMs) to recognise audio shapes. The novelty of the system is in the use of piezo microphones which are low cost, light-weight and portable, and the main investigation is around determining whether these microphones are able to provide suﬃciently rich information to recognise the audio shapes mentioned in such a framework.

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Candel, Ramón Antonio José. "Verificación automática de locutores aplicando pruebas diagnósticas múltiples en serie y en paralelo basadas en DTW (Dynamic Time Warping) y NFCC (Mel-Frequency Cepstral coefficients)." Doctoral thesis, Universidad de Murcia, 2015. http://hdl.handle.net/10803/300433.

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La presente Tesis Doctoral consiste en el diseño de un sistema capaz de realizar tareas de verificación automática de locutores, para lo cual se basa en el modelado mediante los procedimientos DTW (Dynamic Time Warping) y MFCC (Mel-Frequency Cepstral Coefficients). Una vez diseñado éste, se ha evaluado el sistema de forma tanto a nivel de pruebas individuales, DTW y MFCC por separado, como múltiples, combinación de ambas en serie y en paralelo, para grabaciones obtenidas de la base de datos AHUMADA de la Guardia Civil. Todos los resultados han sido vistos teniendo en cuenta la significación estadística de los mismos, derivada de la realización de un determinado número finito de pruebas. Se han obtenido resultados estadísticos de dicho sistema para diferentes tamaños de las bases de datos utilizadas, lo que nos permite concluir la influencia de estos en el método. Como conclusión a los mismos, podemos identificar cuál es el mejor sistema, compuesto por el tipo de modelo y el tamaño de la muestra, que debemos utilizar en un estudio forense en función de la finalidad perseguida.
The present thesis is the design of a system capable of performing automatic speaker verification, for which is based on modeling using the DTW (Dynamic Time Warping) and procedures MFCC (Mel-Frequency Cepstral Coefficients). Once designed it, we have evaluated the system so both at individual events, DTW and MFCC separately as multiple, combining both in series and in parallel, to recordings obtained from the data base AHUMADA from the Guardia Civil. All results have been seen considering the statistical significance thereof, derived from performing a given finite number of tests. Statistical results have been obtained in such a system for different sizes of the databases used, allowing us to conclude the influence of these in the method in order to fix a priori the different variables of this, in order to make the best possible study. To the same conclusion, we can identify what is the best system, consisting of model type and sample size, we use a forensic study based on the intended purpose.

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Lindstål, Tim, and Daniel Marklund. "Application of LabVIEW and myRIO to voice controlled home automation." Thesis, Uppsala universitet, Signaler och System, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-380866.

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The aim of this project is to use NI myRIO and LabVIEW for voice controlled home automation. The NI myRIO is an embedded device which has a Xilinx FPGA and a dual-core ARM Cortex-A9processor as well as analog input/output and digital input/output, and is programmed with theLabVIEW, a graphical programming language. The voice control is implemented in two differentsystems. The first system is based on an Amazon Echo Dot for voice recognition, which is acommercial smart speaker developed by Amazon Lab126. The Echo Dot devices are connectedvia the Internet to the voice-controlled intelligent personal assistant service known as Alexa(developed by Amazon), which is capable of voice interaction, music playback, and controllingsmart devices for home automation. This system in the present thesis project is more focusingon myRIO used for the wireless control of smart home devices, where smart lamps, sensors,speakers and a LCD-display was implemented. The other system is more focusing on myRIO for speech recognition and was built on myRIOwith a microphone connected. The speech recognition was implemented using mel frequencycepstral coefficients and dynamic time warping. A few commands could be recognized, includinga wake word ”Bosse” as well as other four commands for controlling the colors of a smart lamp. The thesis project is shown to be successful, having demonstrated that the implementation ofhome automation using the NI myRIO with two voice-controlled systems can correctly controlhome devices such as smart lamps, sensors, speakers and a LCD-display.

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Larsson, Alm Kevin. "Automatic Speech Quality Assessment in Unified Communication : A Case Study." Thesis, Linköpings universitet, Programvara och system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-159794.

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Speech as a medium for communication has always been important in its ability to convey our ideas, personality and emotions. It is therefore not strange that Quality of Experience (QoE) becomes central to any business relying on voice communication. Using Unified Communication (UC) systems, users can communicate with each other in several ways using many different devices, making QoE an important aspect for such systems. For this thesis, automatic methods for assessing speech quality of the voice calls in Briteback’s UC application is studied, including a comparison of the researched methods. Three methods all using a Gaussian Mixture Model (GMM) as a regressor, paired with extraction of Human Factor Cepstral Coefficients (HFCC), Gammatone Frequency Cepstral Coefficients (GFCC) and Modified Mel Frequency Cepstrum Coefficients (MMFCC) features respectively is studied. The method based on HFCC feature extraction shows better performance in general compared to the two other methods, but all methods show comparatively low performance compared to literature. This most likely stems from implementation errors, showing the difference between theory and practice in the literature, together with the lack of reference implementations. Further work with practical aspects in mind, such as reference implementations or verification tools can make the field more popular and increase its use in the real world.

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Neville, Katrina Lee, and katrina neville@rmit edu au. "Channel Compensation for Speaker Recognition Systems." RMIT University. Electrical and Computer Engineering, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080514.093453.

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This thesis attempts to address the problem of how best to remedy different types of channel distortions on speech when that speech is to be used in automatic speaker recognition and verification systems. Automatic speaker recognition is when a person's voice is analysed by a machine and the person's identity is worked out by the comparison of speech features to a known set of speech features. Automatic speaker verification is when a person claims an identity and the machine determines if that claimed identity is correct or whether that person is an impostor. Channel distortion occurs whenever information is sent electronically through any type of channel whether that channel is a basic wired telephone channel or a wireless channel. The types of distortion that can corrupt the information include time-variant or time-invariant filtering of the information or the addition of 'thermal noise' to the information, both of these types of distortion can cause varying degrees of error in information being received and analysed. The experiments presented in this thesis investigate the effects of channel distortion on the average speaker recognition rates and testing the effectiveness of various channel compensation algorithms designed to mitigate the effects of channel distortion. The speaker recognition system was represented by a basic recognition algorithm consisting of: speech analysis, extraction of feature vectors in the form of the Mel-Cepstral Coefficients, and a classification part based on the minimum distance rule. Two types of channel distortion were investigated: Convolutional (or lowpass filtering) effects Addition of white Gaussian noise Three different methods of channel compensation were tested: Cepstral Mean Subtraction (CMS) RelAtive SpecTrAl (RASTA) Processing Constant Modulus Algorithm (CMA) The results from the experiments showed that for both CMS and RASTA processing that filtering at low cutoff frequencies, (3 or 4 kHz), produced improvements in the average speaker recognition rates compared to speech with no compensation. The levels of improvement due to RASTA processing were higher than the levels achieved due to the CMS method. Neither the CMS or RASTA methods were able to improve accuracy of the speaker recognition system for cutoff frequencies of 5 kHz, 6 kHz or 7 kHz. In the case of noisy speech all methods analysed were able to compensate for high SNR of 40 dB and 30 dB and only RASTA processing was able to compensate and improve the average recognition rate for speech corrupted with a high level of noise (SNR of 20 dB and 10 dB).

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Alvarenga, Rodrigo Jorge. "Reconhecimento de comandos de voz por redes neurais." Universidade de Taubaté, 2012. http://www.bdtd.unitau.br/tedesimplificado/tde_busca/arquivo.php?codArquivo=587.

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Sistema de reconhecimento de fala tem amplo emprego no universo industrial, no aperfeiçoamento de operações e procedimentos humanos e no setor do entretenimento e recreação. O objetivo específico do trabalho foi conceber e desenvolver um sistema de reconhecimento de voz, capaz de identificar comandos de voz, independentemente do locutor. A finalidade precípua do sistema é controlar movimentos de robôs, com aplicações na indústria e no auxílio de deficientes físicos. Utilizou-se a abordagem da tomada de decisão por meio de uma rede neural treinada com as características distintivas do sinal de fala de 16 locutores. As amostras dos comandos foram coletadas segundo o critério de conveniência (em idade e sexo), a fim de garantir uma maior discriminação entre as características de voz, e assim alcançar a generalização da rede neural utilizada. O préprocessamento consistiu na determinação dos pontos extremos da locução do comando e na filtragem adaptativa de Wiener. Cada comando de fala foi segmentado em 200 janelas, com superposição de 25% . As features utilizadas foram a taxa de cruzamento de zeros, a energia de curto prazo e os coeficientes ceptrais na escala de frequência mel. Os dois primeiros coeficientes da codificação linear preditiva e o seu erro também foram testados. A rede neural empregada como classificador foi um perceptron multicamadas, treinado pelo algoritmo backpropagation. Várias experimentações foram realizadas para a escolha de limiares, valores práticos, features e configurações da rede neural. Os resultados foram considerados muito bons, alcançando uma taxa de acertos de 89,16%, sob as condições de pior caso da amostragem dos comandos.
Systems for speech recognition have widespread use in the industrial universe, in the improvement of human operations and procedures and in the area of entertainment and recreation. The specific objective of this study was to design and develop a voice recognition system, capable of identifying voice commands, regardless of the speaker. The main purpose of the system is to control movement of robots, with applications in industry and in aid of disabled people. We used the approach of decision making, by means of a neural network trained with the distinctive features of the speech of 16 speakers. The samples of the voice commands were collected under the criterion of convenience (age and sex), to ensure a greater discrimination between the voice characteristics and to reach the generalization of the neural network. Preprocessing consisted in the determination of the endpoints of each command signal and in the adaptive Wiener filtering. Each speech command was segmented into 200 windows with overlapping of 25%. The features used were the zero crossing rate, the short-term energy and the mel-frequency ceptral coefficients. The first two coefficients of the linear predictive coding and its error were also tested. The neural network classifier was a multilayer perceptron, trained by the backpropagation algorithm. Several experiments were performed for the choice of thresholds, practical values, features and neural network configurations. Results were considered very good, reaching an acceptance rate of 89,16%, under the `worst case conditions for the sampling of the commands.

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Larsson, Joel. "Optimizing text-independent speaker recognition using an LSTM neural network." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-26312.

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In this paper a novel speaker recognition system is introduced. Automated speaker recognition has become increasingly popular to aid in crime investigations and authorization processes with the advances in computer science. Here, a recurrent neural network approach is used to learn to identify ten speakers within a set of 21 audio books. Audio signals are processed via spectral analysis into Mel Frequency Cepstral Coefficients that serve as speaker specific features, which are input to the neural network. The Long Short-Term Memory algorithm is examined for the first time within this area, with interesting results. Experiments are made as to find the optimum network model for the problem. These show that the network learns to identify the speakers well, text-independently, when the recording situation is the same. However the system has problems to recognize speakers from different recordings, which is probably due to noise sensitivity of the speech processing algorithm in use.

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Book chapters on the topic "Mel-Frequency Cepstral coefficients"

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Sueur, Jérôme. "Mel-Frequency Cepstral and Linear Predictive Coefficients." In Sound Analysis and Synthesis with R, 381–98. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77647-7_12.

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Srivastava, Sumit, Mahesh Chandra, and G. Sahoo. "Phase Based Mel Frequency Cepstral Coefficients for Speaker Identification." In Advances in Intelligent Systems and Computing, 309–16. New Delhi: Springer India, 2016. http://dx.doi.org/10.1007/978-81-322-2757-1_31.

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Karahoda, Bertan, Krenare Pireva, and Ali Shariq Imran. "Mel Frequency Cepstral Coefficients Based Similar Albanian Phonemes Recognition." In Human Interface and the Management of Information: Information, Design and Interaction, 491–500. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40349-6_47.

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Ezeiza, Aitzol, Karmele López de Ipiña, Carmen Hernández, and Nora Barroso. "Combining Mel Frequency Cepstral Coefficients and Fractal Dimensions for Automatic Speech Recognition." In Advances in Nonlinear Speech Processing, 183–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25020-0_24.

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Palo, Hemanta Kumar, Mahesh Chandra, and Mihir Narayan Mohanty. "Recognition of Human Speech Emotion Using Variants of Mel-Frequency Cepstral Coefficients." In Advances in Systems, Control and Automation, 491–98. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4762-6_47.

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Husain, Moula, S. M. Meena, and Manjunath K. Gonal. "Speech Based Arithmetic Calculator Using Mel-Frequency Cepstral Coefficients and Gaussian Mixture Models." In Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics, 209–18. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2538-6_22.

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Benkedjouh, Tarak, Taha Chettibi, Yassine Saadouni, and Mohamed Afroun. "Gearbox Fault Diagnosis Based on Mel-Frequency Cepstral Coefficients and Support Vector Machine." In Computational Intelligence and Its Applications, 220–31. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89743-1_20.

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Traboulsi, Ahmad, and Michel Barbeau. "Identification of Drone Payload Using Mel-Frequency Cepstral Coefficients and LSTM Neural Networks." In Proceedings of the Future Technologies Conference (FTC) 2020, Volume 1, 402–12. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63128-4_30.

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Xue, Yang. "Speaker Recognition System Using Dynamic Time Warping Matching and Mel-Scale Frequency Cepstral Coefficients." In Lecture Notes in Electrical Engineering, 961–67. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8411-4_127.

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Dhonde, S. B., Amol Chaudhari, and S. M. Jagade. "Integration of Mel-frequency Cepstral Coefficients with Log Energy and Temporal Derivatives for Text-Independent Speaker Identification." In Proceedings of the International Conference on Data Engineering and Communication Technology, 791–97. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1675-2_78.

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Conference papers on the topic "Mel-Frequency Cepstral coefficients"

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Jithendra, Uppu, Usha Mittal, and Priyanka Chawla. "Audio Detection using Mel-frequency Cepstral Coefficients." In 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). IEEE, 2021. http://dx.doi.org/10.1109/icrito51393.2021.9596443.

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Nguyen Viet Cuong, Vu Dinh, and Lam Si Tung Ho. "Mel-frequency Cepstral Coefficients for Eye Movement Identification." In 2012 IEEE 24th International Conference on Tools with Artificial Intelligence (ICTAI 2012). IEEE, 2012. http://dx.doi.org/10.1109/ictai.2012.42.

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Zhou, Xinhui, Daniel Garcia-Romero, Ramani Duraiswami, Carol Espy-Wilson, and Shihab Shamma. "Linear versus mel frequency cepstral coefficients for speaker recognition." In Understanding (ASRU). IEEE, 2011. http://dx.doi.org/10.1109/asru.2011.6163888.

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Thaine, Patricia, and Gerald Penn. "Extracting Mel-Frequency and Bark-Frequency Cepstral Coefficients from Encrypted Signals." In Interspeech 2019. ISCA: ISCA, 2019. http://dx.doi.org/10.21437/interspeech.2019-1136.

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Ramirez, Angel David Pedroza, Jose Ismael de la Rosa Vargas, Rogelio Rosas Valdez, and Aldonso Becerra. "A comparative between Mel Frequency Cepstral Coefficients (MFCC) and Inverse Mel Frequency Cepstral Coefficients (IMFCC) features for an Automatic Bird Species Recognition System." In 2018 IEEE Latin American Conference on Computational Intelligence (LA-CCI). IEEE, 2018. http://dx.doi.org/10.1109/la-cci.2018.8625230.

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Talal, T. M., and Ayman El-Sayed. "Identification of satellite images based on mel frequency cepstral coefficients." In Systems (ICCES). IEEE, 2009. http://dx.doi.org/10.1109/icces.2009.5383270.

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Korkmaz, Onur Erdem, and Ayten Atasoy. "Emotion recognition from speech signal using mel-frequency cepstral coefficients." In 2015 9th International Conference on Electrical and Electronics Engineering (ELECO). IEEE, 2015. http://dx.doi.org/10.1109/eleco.2015.7394435.

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Jokic, Ivan D., Stevan D. Jokic, Vlado D. Delic, and Zoran H. Perie. "Mel-frequency cepstral coefficients as features for automatic speaker recognition." In 2015 23rd Telecommunications Forum Telfor (TELFOR). IEEE, 2015. http://dx.doi.org/10.1109/telfor.2015.7377497.

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Lin, Ming, Shangping Zhong, and Lingli Lin. "Chicken Sound Recognition Using Anti-noise Mel Frequency Cepstral Coefficients." In 2015 Third International Conference on Robot, Vision and Signal Processing (RVSP). IEEE, 2015. http://dx.doi.org/10.1109/rvsp.2015.60.

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Cooney, Ciaran, Rafaella Folli, and Damien Coyle. "Mel Frequency Cepstral Coefficients Enhance Imagined Speech Decoding Accuracy from EEG." In 2018 29th Irish Signals and Systems Conference (ISSC). IEEE, 2018. http://dx.doi.org/10.1109/issc.2018.8585291.

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