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Relevant bibliographies by topics / Mel-Frequency Cepstral coefficients

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

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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

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Ajinurseto, Galih, La Ode Bakrim, and Nur Islamuddin. "Penerapan Metode Mel Frequency Cepstral Coefficients pada Sistem Pengenalan Suara Berbasis Desktop." Infomatek 25, no. 1 (2023): 11–20. http://dx.doi.org/10.23969/infomatek.v25i1.6109.

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Teknologi biometrik sedang menjadi tren teknologi dalam berbagai bidang kehidupan. Teknologi biometrik memanfaatkan bagian tubuh manusia sebagai alat ukur sistem yang memiliki keunikan disetiap individu. Suara merupakan bagian tubuh manusia yang memiliki keunikan dan cocok dijadikan sebagai alat ukur dalam sistem yang mengadopsi teknologi biometrik. Sistem pengenalan suara adalah salah satu penerapan teknologi biometrik yang fokus kepada suara manusia. Sistem pengenalan suara memerlukan metode ekstraksi fitur, salah satu metode ekstraksi fitur adalah metode 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 (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 (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 (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

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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 (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 (GFCC

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Elizarov, D. A., P. A. Ashaeva, and E. A. Stepanova. "Voice authentication module using mel-cepstral coefficients." Herald of Dagestan State Technical University. Technical Sciences 51, no. 2 (2024): 77–82. http://dx.doi.org/10.21822/2073-6185-2024-51-2-77-82.

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Objective. The purpose of the study is to develop and apply a method for extracting information about the identity of users from recordings of their voices using the calculation of mel-cepstral coefficients.Method. In the study of the application of methods for extracting informative features from a voice recording, allowing identification of the speaker, an authentication scheme using mel-cepstral coefficients is presented.Result. Based on this method, an authentication module was implemented using audio recordings of user voices using the simplest MFCC. The authentication module was develope

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Ma, Liqiang, Anqi Jiang, and Wanlu Jiang. "The Intelligent Diagnosis of a Hydraulic Plunger Pump Based on the MIGLCC-DLSTM Method Using Sound Signals." Machines 12, no. 12 (2024): 869. https://doi.org/10.3390/machines12120869.

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To fully exploit the rich state and fault information embedded in the acoustic signals of a hydraulic plunger pump, this paper proposes an intelligent diagnostic method based on sound signal analysis. First, acoustic signals were collected under normal and various fault conditions. Then, four distinct acoustic features—Mel Frequency Cepstral Coefficients (MFCCs), Inverse Mel Frequency Cepstral Coefficients (IMFCCs), Gammatone Frequency Cepstral Coefficients (GFCCs), and Linear Prediction Cepstral Coefficients (LPCCs)—were extracted and integrated into a novel hybrid cepstral feature called MIG

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Yan, Hao, Huajun Bai, Xianbiao Zhan, Zhenghao Wu, Liang Wen, and Xisheng Jia. "Combination of VMD Mapping MFCC and LSTM: A New Acoustic Fault Diagnosis Method of Diesel Engine." Sensors 22, no. 21 (2022): 8325. http://dx.doi.org/10.3390/s22218325.

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Diesel engines have a wide range of functions in the industrial and military fields. An urgent problem to be solved is how to diagnose and identify their faults effectively and timely. In this paper, a diesel engine acoustic fault diagnosis method based on variational modal decomposition mapping Mel frequency cepstral coefficients (MFCC) and long-short-term memory network is proposed. Variational mode decomposition (VMD) is used to remove noise from the original signal and differentiate the signal into multiple modes. The sound pressure signals of different modes are mapped to the Mel filter b

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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 (2020): 3209. http://dx.doi.org/10.18494/sam.2020.2860.

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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 Aer

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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 secu

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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<br>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

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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 es

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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 pers

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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.

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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 wh

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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

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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, wit

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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. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77647-7_12.

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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. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40349-6_47.

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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. Springer India, 2016. http://dx.doi.org/10.1007/978-81-322-2757-1_31.

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Dash, Yajnaseni, Ajith Abraham, Shivam Gupta, Shaurya Vardhan Rathore, and Harsh Patil. "Enhancing Respiratory Monitoring by CNN Using Mel Frequency Cepstral Coefficients." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-81080-0_54.

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Mashika, Mpho, and Dustin van der Haar. "Mel Frequency Cepstral Coefficients and Support Vector Machines for Cough Detection." In Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35748-0_18.

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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. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4762-6_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. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25020-0_24.

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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. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63128-4_30.

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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. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89743-1_20.

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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. Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2538-6_22.

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

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Akilandeswari, T., D. Aashritha, J. S. Athibathi Raja, A. Tanuja, and J. Dhinisha. "Feature Enriched Speech Emotion Recognition Using Mel Frequency Cepstral Coefficients." In 2025 International Conference on Machine Learning and Autonomous Systems (ICMLAS). IEEE, 2025. https://doi.org/10.1109/icmlas64557.2025.10968799.

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Santoso, Tri Arief Sardjono, and Djoko Purwanto. "Optimizing Mel-Frequency Cepstral Coefficients for Improved Robot Speech Command Recognition Accuracy." In 2024 International Seminar on Application for Technology of Information and Communication (iSemantic). IEEE, 2024. https://doi.org/10.1109/isemantic63362.2024.10762627.

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S, Geerthik, Senthil G. A, Jayashree D, and Abinaya J. "Deepfake Video Prediction Using Attention-Based CNN and Mel-Frequency Cepstral Coefficients." In 2024 Third International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT). IEEE, 2024. http://dx.doi.org/10.1109/iceeict61591.2024.10718393.

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Bhushan, Shourya, and Manish Chaturvedi. "Emergency Vehicle Direction Detection Using Mel-Frequency Cepstral Coefficients and Deep Learning." In 2024 IEEE International Conference on Vehicular Electronics and Safety (ICVES). IEEE, 2024. https://doi.org/10.1109/icves61986.2024.10928001.

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Martin, Noel, Sharun Raj Nambayil, Devikrishna U, Joel Jismon P, and Fasila K.A. "Multimodal Deepfake Detection using Deep-Convolutional Neural Networks and Mel-Frequency Cepstral Coefficients." In 2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES). IEEE, 2024. https://doi.org/10.1109/spices62143.2024.10779815.

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Ma, Chi-Yuan, Shu-Ya Jin, Ya-Xian Fan, and Zhi-Yong Tao. "Offshore ship classification based on Mel-frequency Cepstral Coefficients of main intrinsic mode." In 2024 4th International Conference on Electronic Information Engineering and Computer Communication (EIECC). IEEE, 2024. https://doi.org/10.1109/eiecc64539.2024.10929488.

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Sudharsana, P. P., R. R. Rajalaxmi, R. Gughan, R. Thamilselvan, S. Mohana Saranya, and K. Sruthi. "Unmasking Audio Deception: Performance Analysis in Machine Learning Models with Mel-Frequency and Gammatone Frequency Cepstral Coefficients." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725767.

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Teja, Beeram Bhanu, Madan Lal Saini, Edupalli Greeshmanth Kumar, and Syed Abbas Khadar Ali. "Utilizing Artificial Neural Networks and Mel-Frequency Cepstral Coefficients for Gender Identification from Voice Data." In 2024 8th International Conference on Computational System and Information Technology for Sustainable Solutions (CSITSS). IEEE, 2024. https://doi.org/10.1109/csitss64042.2024.10816760.

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Gerhana, Yana Aditia, Muhammad Reza Abdul Fatah, Agung Wahana, Muhammad Insan Al Amin, and Ichsan Budiman. "Classification of Human Voice Types Using Convolutional Neural Network Algorithm with Extraction of Mel-Frequency Cepstral Coefficients." In 2024 12th International Conference on Cyber and IT Service Management (CITSM). IEEE, 2024. https://doi.org/10.1109/citsm64103.2024.10775826.

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Wang, Wei, Ruilin Wang, Qiangqing Liu, Ting Fang, and Dongao Li. "Fault diagnosis of motor bearing based on Mel-scale frequency cepstral coefficients and 1D-convolutional neural network." In Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024), edited by Ji Zhao and Yonghui Yang. SPIE, 2024. http://dx.doi.org/10.1117/12.3037844.

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