Relevant bibliographies by topics / Mel-frequency Cepstrum Coefficients (MFCC)

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Mel-frequency Cepstrum Coefficients (MFCC)'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Mel-frequency Cepstrum Coefficients (MFCC)"

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Mahalakshmi, P., Muruganandam M, and Sharmila A. "VOICE RECOGNITION SECURITY SYSTEM USING MEL-FREQUENCY CEPSTRUM COEFFICIENTS." Asian Journal of Pharmaceutical and Clinical Research 9, no. 9 (2016): 131. http://dx.doi.org/10.22159/ajpcr.2016.v9s3.13633.

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ABSTRACTObjective: Voice Recognition is a fascinating field spanning several areas of computer science and mathematics. Reliable speaker recognition is a hardproblem, requiring a combination of many techniques; however modern methods have been able to achieve an impressive degree of accuracy. Theobjective of this work is to examine various speech and speaker recognition techniques and to apply them to build a simple voice recognition system.Method: The project is implemented on software which uses different techniques such as Mel frequency Cepstrum Coefficient (MFCC), VectorQuantization (VQ) w
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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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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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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 (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
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Rasyid, Muhammad Fahim, Herlina Jayadianti, and Herry Sofyan. "APLIKASI PENGENALAN PENUTUR PADA IDENTIFIKASI SUARA PENELEPON MENGGUNAKAN MEL-FREQUENCY CEPSTRAL COEFFICIENT DAN VECTOR QUANTIZATION (Studi Kasus : Layanan Hotline Universitas Pembangunan Nasional “Veteran” Yogyakarta)." Telematika 17, no. 2 (2020): 68. http://dx.doi.org/10.31315/telematika.v1i1.3380.

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Layanan hotline Universitas Pembangunan Nasional “Veteran” Yogyakarta merupakan layanan yang dapat digunakan oleh semua orang. Layanan tersebut digunakan dosen dan pegawai untuk berbagi informasi dengan bagian-bagian yang berlokasi di gedung rektorat. Penelepon dapat berkomunikasi dengan bagian yang dituju apabila telah teridentifikasi oleh petugas layanan hotline. Terminologi identitas yang terdiri dari nama, jabatan serta asal jurusan atau bagian ditanyakan saat proses identifikasi. Tidak terdapat catatan hasil identifikasi penelepon baik dalam bentuk fisik maupun basis data yang terekam pad
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Yang, Xing Hai, Wen Jie Fu, Yu Tai Wang, Jia Ding, and Chang Zhi Wei. "Heart Sound Clustering Based on Supervised Kohonen Network." Applied Mechanics and Materials 138-139 (November 2011): 1115–20. http://dx.doi.org/10.4028/www.scientific.net/amm.138-139.1115.

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In this paper, a new method based on Supervised Kohonen network (SKN) and Mel-frequency cepstrum coefficients (MFCC) is introduced. MFCC of heart sound signal are extracted firstly, and then features are got by calculating every order of MFCC average energy. Finally, SKN is used to identify heart sound. The experimental result shows that this algorithm has a good performance in heart sound clustering, and is of significant practical value.
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de Souza, Edson Florentino, Túlio Nogueira Bittencourt, Diogo Ribeiro, and Hermes Carvalho. "Feasibility of Applying Mel-Frequency Cepstral Coefficients in a Drive-by Damage Detection Methodology for High-Speed Railway Bridges." Sustainability 14, no. 20 (2022): 13290. http://dx.doi.org/10.3390/su142013290.

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In this paper, a drive-by damage detection methodology for high-speed railway (HSR) bridges is addressed, to appraise the application of Mel-frequency cepstral coefficients (MFCC) to extract the Damage Index (DI). A finite element (FEM) 2D VTBI model that incorporates the train, ballasted track and bridge behavior is presented. The formulation includes track irregularities and a damaged condition induced in a specified structure region. The feasibility of applying cepstrum analysis components to the indirect damage detection in HSR by on-board sensors is evaluated by numerical simulations, in
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Sasilo, Ababil Azies, Rizal Adi Saputra, and Ika Purwanti Ningrum. "Sistem Pengenalan Suara Dengan Metode Mel Frequency Cepstral Coefficients Dan Gaussian Mixture Model." Komputika : Jurnal Sistem Komputer 11, no. 2 (2022): 203–10. http://dx.doi.org/10.34010/komputika.v11i2.6655.

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ABSTRAK – 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 dan metode klasifikasi, salah satu metode ekstraksi fitur adalah MFCC. MFCC
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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 the
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Zhang, Lanyue, Di Wu, Xue Han, and Zhongrui Zhu. "Feature Extraction of Underwater Target Signal Using Mel Frequency Cepstrum Coefficients Based on Acoustic Vector Sensor." Journal of Sensors 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/7864213.

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Feature extraction method using Mel frequency cepstrum coefficients (MFCC) based on acoustic vector sensor is researched in the paper. Signals of pressure are simulated as well as particle velocity of underwater target, and the features of underwater target using MFCC are extracted to verify the feasibility of the method. The experiment of feature extraction of two kinds of underwater targets is carried out, and these underwater targets are classified and recognized by Backpropagation (BP) neural network using fusion of multi-information. Results of the research show that MFCC, first-order dif
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Dissertations / Theses on the topic "Mel-frequency Cepstrum Coefficients (MFCC)"

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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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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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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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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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Pešek, Milan. "Detekce logopedických vad v řeči." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-218106.

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The thesis deals with a design and an implementation of software for a detection of logopaedia defects of speech. Due to the need of early logopaedia defects detecting, this software is aimed at a child’s age speaker. The introductory part describes the theory of speech realization, simulation of speech realization for numerical processing, phonetics, logopaedia and basic logopaedia defects of speech. There are also described used methods for feature extraction, for segmentation of words to speech sounds and for features classification into either correct or incorrect pronunciation class. In t
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Sklar, Alexander Gabriel. "Channel Modeling Applied to Robust Automatic Speech Recognition." Scholarly Repository, 2007. http://scholarlyrepository.miami.edu/oa_theses/87.

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In automatic speech recognition systems (ASRs), training is a critical phase to the system?s success. Communication media, either analog (such as analog landline phones) or digital (VoIP) distort the speaker?s speech signal often in very complex ways: linear distortion occurs in all channels, either in the magnitude or phase spectrum. Non-linear but time-invariant distortion will always appear in all real systems. In digital systems we also have network effects which will produce packet losses and delays and repeated packets. Finally, one cannot really assert what path a signal will take, and
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Bekli, Zeid, and William Ouda. "A performance measurement of a Speaker Verification system based on a variance in data collection for Gaussian Mixture Model and Universal Background Model." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20122.

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Voice recognition has become a more focused and researched field in the last century,and new techniques to identify speech has been introduced. A part of voice recognition isspeaker verification which is divided into Front-end and Back-end. The first componentis the front-end or feature extraction where techniques such as Mel-Frequency CepstrumCoefficients (MFCC) is used to extract the speaker specific features of a speech signal,MFCC is mostly used because it is based on the known variations of the humans ear’scritical frequency bandwidth. The second component is the back-end and handles thes
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Kufa, Tomáš. "Rozpoznáváni standardních PILOT-CONTROLLER řídicích povelů v hlasové podobě." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-217849.

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The subject of this graduation thesis is an application of speech recognition into ATC commands. The selection of methods and approaches to automatic recognition of ATC commands rises from detailed air traffic studies. By the reason that there is not any definite solution in such extensive field like speech recognition, this diploma work is focused just on speech recognizer based on comparison with templates (DTW). This recognizor is in this thesis realized and compared with freely accessible HTK system from Cambrige University based on statistic methods making use of Hidden Markov models. The
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Book chapters on the topic "Mel-frequency Cepstrum Coefficients (MFCC)"

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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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Hamzah, Raseeda, Nursuriati Jamil, and Noraini Seman. "Filled Pause Classification Using Energy-Boosted Mel-Frequency Cepstrum Coefficients." In Lecture Notes in Electrical Engineering. Springer Singapore, 2014. http://dx.doi.org/10.1007/978-981-4585-42-2_36.

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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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Xiong, Ziyou, and Thomas S. Huang. "Boosting Speech/Non-speech Classi.cation Using Averaged Mel-Frequency Cepstrum Coefficients Features." In Advances in Multimedia Information Processing — PCM 2002. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36228-2_71.

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Preetham, Manoj, Jemimah Beulah Panga, J. Andrew, Kumudha Raimond, and Hien Dang. "Classification of Music Genres Based on Mel-Frequency Cepstrum Coefficients Using Deep Learning Models." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2177-3_83.

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Shanmugam, Mohana, Nur Nesa Nashuha Ismail, Pritheega Magalingam, Nik Nur Wahidah Nik Hashim, and Dalbir Singh. "Understanding the Use of Acoustic Measurement and Mel Frequency Cepstral Coefficient (MFCC) Features for the Classification of Depression Speech." In Current and Future Trends on Intelligent Technology Adoption. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-48397-4_17.

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Trabelsi, Imen, and Med Salim Bouhlel. "Comparison of Several Acoustic Modeling Techniques for Speech Emotion Recognition." In Cognitive Analytics. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2460-2.ch015.

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Automatic Speech Emotion Recognition (SER) is a current research topic in the field of Human Computer Interaction (HCI) with a wide range of applications. The purpose of speech emotion recognition system is to automatically classify speaker's utterances into different emotional states such as disgust, boredom, sadness, neutral, and happiness. The speech samples in this paper are from the Berlin emotional database. Mel Frequency cepstrum coefficients (MFCC), Linear prediction coefficients (LPC), linear prediction cepstrum coefficients (LPCC), Perceptual Linear Prediction (PLP) and Relative Spec
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Hashemi Amjad, Arabalibeik Hossein, and Agin Khosrow. "Classification of Wheeze Sounds Using Cepstral Analysis and Neural Networks." In Studies in Health Technology and Informatics. IOS Press, 2012. https://doi.org/10.3233/978-1-61499-022-2-161.

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Wheezes are abnormal, continuous sounds heard over large airways and chest. They are divided to two groups based on relative intensity of airway obstruction. They are usually heard in asthma, pneumonia, emphysema and chronic obstructive pulmonary diseases (COPD). We present a classification method to discriminate between polyphonic and monophonic wheeze sounds using multilayer perceptron (MLP) neural network and mel-frequency cepstral coefficients (MFCC). Wheeze signals are divided to segments with 50% overlap. MFCC features are then extracted. Groups with different numbers of MFCC powerful fe
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Coskun, Huseyin, and Tuncay Yigit. "Artificial Intelligence Applications on Classification of Heart Sounds." In Nature-Inspired Intelligent Techniques for Solving Biomedical Engineering Problems. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-4769-3.ch007.

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The aim of this chapter is to classify normal and extra systole heart sounds using artificial intelligence methods. Initially, both heart sounds have been passed from Butterworth, Chebyshev, Elliptic digital filter in specific frequency values to remove noise. Afterwards, features of heart sounds have been obtained for classification. For this process, wavelet transform and Mel-frequency cepstral coefficients (MFCC) methods have been applied. Training and test data have been created for classifier by taking means and standard deviation of gained feature. Support vector machine (SVM) and artifi
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Mohamed, Walid, and Yosssra Ben Fadhel. "Speech Recognition System Implementation of a Method Based on Wave Atom Transform and Frequency-Mel Cepstral Coefficients Using SVM." In Applications of Encryption and Watermarking for Information Security. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-4945-5.ch009.

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In the field of human-machine interaction, automatic speech recognition (ASR) has been a prominent research area since the 1950s. Single-word speech recognition is widely used in voice command systems, which can be implemented in various applications such as access control systems, robots, and voice-enabled devices. This study describes the implementation of a single-word speech recognition system using wave atoms transform (WAT) and frequency-mel cepstral coefficients (MFCC) on a Raspberry Pi 3 (RPi 3) board. The WAT-MFCC approach is combined with a support vector machine (SVM). The experimen
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Conference papers on the topic "Mel-frequency Cepstrum Coefficients (MFCC)"

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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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<p>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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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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Lin, Mu, Yang Xuemeng, Qiu Mengran, Hu Chen, and Peng Yuan. "The Programming of Mel Frequency Cepstrum Coefficient (MFCC) Difference's Characteristic Extraction of Parameters and Emulation." In 2015 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS). IEEE, 2015. http://dx.doi.org/10.1109/icitbs.2015.227.

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Martinez, Jorge, Hector Perez, Enrique Escamilla, and Masahisa Mabo Suzuki. "Speaker recognition using Mel frequency Cepstral Coefficients (MFCC) and Vector quantization (VQ) techniques." In 2012 22nd International Conference on Electrical Communications and Computers (CONIELECOMP). IEEE, 2012. http://dx.doi.org/10.1109/conielecomp.2012.6189918.

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Nurahmad, Chairunissa Atimas, and Mirna Adriani. "Identifying traditional music instruments on polyphonic Indonesian folksong using mel-frequency cepstral coefficients (MFCC)." In the 10th International Conference. ACM Press, 2012. http://dx.doi.org/10.1145/2428955.2428967.

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Chauhan, Paresh M., and Nikita P. Desai. "Mel Frequency Cepstral Coefficients (MFCC) based speaker identification in noisy environment using wiener filter." In 2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE). IEEE, 2014. http://dx.doi.org/10.1109/icgccee.2014.6921394.

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Rahmandani, Muhammad, Hanung Adi Nugroho, and Noor Akhmad Setiawan. "Cardiac Sound Classification Using Mel-Frequency Cepstral Coefficients (MFCC) and Artificial Neural Network (ANN)." In 2018 3rd International Conference on Information Technology, Information System and Electrical Engineering (ICITISEE). IEEE, 2018. http://dx.doi.org/10.1109/icitisee.2018.8721007.

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Marlina, Lina, Cipto Wardoyo, W. S. Mada Sanjaya, et al. "Makhraj recognition of Hijaiyah letter for children based on Mel-Frequency Cepstrum Coefficients (MFCC) and Support Vector Machines (SVM) method." In 2018 International Conference on Information and Communications Technology (ICOIACT). IEEE, 2018. http://dx.doi.org/10.1109/icoiact.2018.8350684.

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Anggraeni, Dyah, W. S. Mada Sanjaya, Madinatul Munawwaroh, M. Yusuf Solih Nurasyidiek, and Ikhsan Purnama Santika. "Control of robot arm based on speech recognition using Mel-Frequency Cepstrum Coefficients (MFCC) and K-Nearest Neighbors (KNN) method." In 2017 International Conference on Advanced Mechatronics, Intelligent Manufacture, and Industrial Automation (ICAMIMIA). IEEE, 2017. http://dx.doi.org/10.1109/icamimia.2017.8387590.

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Abbiyansyah, Mochammad Zava, and Fitri Utaminingrum. "Voice Recognition on Humanoid Robot Darwin OP Using Mel Frequency Cepstrum Coefficients (MFCC) Feature and Artificial Neural Networks (ANN) Method." In 2022 2nd International Conference on Information Technology and Education (ICIT&E). IEEE, 2022. http://dx.doi.org/10.1109/icite54466.2022.9759883.

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You might also be interested in the extended bibliographies on the topic 'Mel-frequency Cepstrum Coefficients (MFCC)' for particular source types:
Journal articles
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