Bibliographies thématiques / Mel-Frequency Cepstral Coefficients (MFCCs)

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Littérature scientifique sur le sujet « Mel-Frequency Cepstral Coefficients (MFCCs) »

Auteur : Grafiati
Publié le 7 juin 2025
Mis à jour le 2 août 2025

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Articles de revues sur le sujet "Mel-Frequency Cepstral Coefficients (MFCCs)"

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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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H. Mohd Johari, N., Noreha Abdul Malik, and K. A. Sidek. "Distinctive features for normal and crackles respiratory sounds using cepstral coefficients." Bulletin of Electrical Engineering and Informatics 8, no. 3 (2019): 875–81. http://dx.doi.org/10.11591/eei.v8i3.1517.

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Classification of respiratory sounds between normal and abnormal is very crucial for screening and diagnosis purposes. Lung associated diseases can be detected through this technique. With the advancement of computerized auscultation technology, the adventitious sounds such as crackles can be detected and therefore diagnostic test can be performed earlier. In this paper, Linear Predictive Cepstral Coefficient (LPCC) and Mel-frequency Cepstral Coefficient (MFCC) are used to extract features from normal and crackles respiratory sounds. By using statistical computation such as mean and standard d
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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 result
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Pratiwi, Tika, Andi Sunyoto, and Dhani Ariatmanto. "Music Genre Classification Using K-Nearest Neighbor and Mel-Frequency Cepstral Coefficients." Sinkron 8, no. 2 (2024): 861–67. http://dx.doi.org/10.33395/sinkron.v8i2.12912.

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Music genre classification plays a pivotal role in organizing and accessing vast music collections, enhancing user experiences, and enabling efficient music recommendation systems. This study focuses on employing the K-Nearest Neighbors (KNN) algorithm in conjunction with Mel-Frequency Cepstral Coefficients (MFCCs) for accurate music genre classification. MFCCs extract essential spectral features from audio signals, which serve as robust representations of music characteristics. The proposed approach achieves a commendable classification accuracy of 80%, showcasing the effectiveness of KNN-MFC
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Kasim, Anita Ahmad, Muhammad Bakri, Irwan Mahmudi, Rahmawati Rahmawati, and Zulnabil Zulnabil. "Artificial Intelligent for Human Emotion Detection with the Mel-Frequency Cepstral Coefficient (MFCC)." JUITA : Jurnal Informatika 11, no. 1 (2023): 47. http://dx.doi.org/10.30595/juita.v11i1.15435.

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Emotions are an important aspect of human communication. Expression of human emotions can be identified through sound. The development of voice detection or speech recognition is a technology that has developed rapidly to help improve human-machine interaction. This study aims to classify emotions through the detection of human voices. One of the most frequently used methods for sound detection is the Mel-Frequency Cepstrum Coefficient (MFCC) where sound waves are converted into several types of representation. Mel-frequency cepstral coefficients (MFCCs) are the coefficients that collectively
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Varma, V. Sai Nitin, and Abdul Majeed K.K. "Advancements in Speaker Recognition: Exploring Mel Frequency Cepstral Coefficients (MFCC) for Enhanced Performance in Speaker Recognition." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (2023): 88–98. http://dx.doi.org/10.22214/ijraset.2023.55124.

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Abstract: Speaker recognition, a fundamental capability of software or hardware systems, involves receiving speech signals, identifying the speaker present in the speech signal, and subsequently recognizing the speaker for future interactions. This process emulates the cognitive task performed by the human brain. At its core, speaker recognition begins with speech as the input to the system. Various techniques have been developed for speech recognition, including Mel frequency cepstral coefficients (MFCC), Linear Prediction Coefficients (LPC), Linear Prediction Cepstral coefficients (LPCC), Li
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N., H. Mohd Johari, Abdul Malik Noreha, and A. Sidek K. "Distinctive features for normal and crackles respiratory sounds using cepstral coefficients." Bulletin of Electrical Engineering and Informatics 8, no. 3 (2019): 875–81. https://doi.org/10.11591/eei.v8i3.1517.

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Classification of respiratory sounds between normal and abnormal is very crucial for screening and diagnosis purposes. Lung associated diseases can be detected through this technique. With the advancement of computerized auscultation technology, the adventitious sounds such as crackles can be detected and therefore diagnostic test can be performed earlier. In this paper, Linear Predictive Cepstral Coefficient (LPCC) and Mel-frequency Cepstral Coefficient (MFCC) are used to extract features from normal and crackles respiratory sounds. By using statistical computation such as mean and standard d
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ELSHARKAWY, R. R., M. HINDY, S. EL-RABAIE, and M. I. DESSOUKY. "FET SMALL-SIGNAL MODELING USING MEL-FREQUENCY CEPSTRAL COEFFICIENTS AND THE DISCRETE COSINE TRANSFORM." Journal of Circuits, Systems and Computers 19, no. 08 (2010): 1835–46. http://dx.doi.org/10.1142/s0218126610007158.

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In this paper, a novel neural technique is proposed for FET small-signal modeling. This technique is based on the discrete cosine transform (DCT) and the Mel-frequency cepstral coefficients (MFCCs). The input data to traditional neural systems for FET small-signal modeling are the scattering parameters and the corresponding frequencies in a certain band, and the outputs are the circuit elements. In the proposed technique, the input data are considered random, and the MFCCs are calculated from these inputs and their DCT. The MFCCs are used to give a few features from the input random data seque
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Musab, T. S. Al-Kaltakchi, Abd Al-Raheem Taha Haithem, Abd Shehab Mohanad, and A. M. Abdullah Mohammed. "Comparison of feature extraction and normalization methods for speaker recognition using grid-audiovisual database." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 18, no. 2 (2020): 782–89. https://doi.org/10.11591/ijeecs.v18.i2.pp782-789.

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In this paper, different feature extraction and feature normalization methods are investigated for speaker recognition. With a view to give a good representation of acoustic speech signals, Power Normalized Cepstral Coefficients (PNCCs) and Mel Frequency Cepstral Coefficients (MFCCs) are employed for feature extraction. Then, to mitigate the effect of linear channel, Cepstral Mean-Variance Normalization (CMVN) and feature warping are utilized. The current paper investigates Text-independent speaker identification system by using 16 coefficients from both the MFCCs and PNCCs features. Eight dif
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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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Thèses sur le sujet "Mel-Frequency Cepstral Coefficients (MFCCs)"

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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, 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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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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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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Ulrich, Natalja. "Linguistic and speaker variation in Russian fricatives." Electronic Thesis or Diss., Lyon 2, 2022. http://www.theses.fr/2022LYO20031.

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Cette thèse présente une investigation acoustico-phonétique des détails phonétiques des fricatives russes.L'objectif principal était de détecter des corrélats acoustiques porteurs d'infor- mations linguistiques et idiosyncrasiques. Les questions abordées étaient de savoir si le lieu d'articulation, le sexe du locuteur ou son identité peuvent être prédits par des indices acoustiques et quelles mesures acoustiques représentent les indicateurs les plus fiables. En outre, la distribution des caractéristiques spécifiques au locuteur et à la variation inter et intra locuteur à travers les indices ac
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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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Okuyucu, Cigdem. "Semantic Classification And Retrieval System For Environmental Sounds." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12615114/index.pdf.

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The growth of multimedia content in recent years motivated the research on audio classification and content retrieval area. In this thesis, a general environmental audio classification and retrieval approach is proposed in which higher level semantic classes (outdoor, nature, meeting and violence) are obtained from lower level acoustic classes (emergency alarm, car horn, gun-shot, explosion, automobile, motorcycle, helicopter, wind, water, rain, applause, crowd and laughter). In order to classify an audio sample into acoustic classes, MPEG-7 audio features, Mel Frequency Cepstral Coefficients
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Assaad, Firas Souhail. "Biometric Multi-modal User Authentication System based on Ensemble Classifier." University of Toledo / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1418074931.

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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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Chapitres de livres sur le sujet "Mel-Frequency Cepstral Coefficients (MFCCs)"

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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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Suman, Preetam, Subhdeep Karan, Vrijendra Singh, and R. Maringanti. "Algorithm for Gunshot Detection Using Mel-Frequency Cepstrum Coefficients (MFCC)." In Lecture Notes in Electrical Engineering. Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1823-4_15.

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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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Sulistijono, Indra Adji, Renita Chulafa Urrosyda, and Zaqiatud Darojah. "Mel-Frequency Cepstral Coefficient (MFCC) for Music Feature Extraction for the Dancing Robot Movement Decision." In Intelligent Robotics and Applications. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43518-3_28.

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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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Actes de conférences sur le sujet "Mel-Frequency Cepstral Coefficients (MFCCs)"

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Shen, Zhouhui, Dongdong Chen, and Ye Xia. "Ensemble Learning-based Lightweight Acoustic Approach for Void Detection in Concrete-filled Steel Tubular Arch Bridges." In IABSE Symposium, Tokyo 2025: Environmentally Friendly Technologies and Structures: Focusing on Sustainable Approaches. International Association for Bridge and Structural Engineering (IABSE), 2025. https://doi.org/10.2749/tokyo.2025.0736.

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&lt;p&gt;In concrete-filled steel tubular (CFST) arch bridges, shrinkage induced by axial pressure and temperature is prone to cause air voids at the arch ring, which seriously affects the structural performance. However, traditional shallow machine learning approaches have limited generalization performance, while deep learning models require long iteration times and substantial computational resources. Therefore, this study proposes a lightweight approach for CFST void detection based on ensemble learning and one-dimensional Mel-frequency cepstral coefficients (MFCC). Feature extraction meth
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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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Mawadda Warohma, Ayu, Puspa Kurniasari, Suci Dwijayanti, Irmawan, and Bhakti Yudho Suprapto. "Identification of Regional Dialects Using Mel Frequency Cepstral Coefficients (MFCCs) and Neural Network." In 2018 International Seminar on Application for Technology of Information and Communication (iSemantic). IEEE, 2018. http://dx.doi.org/10.1109/isemantic.2018.8549731.

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