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Dissertations / Theses on the topic 'Mel-Frequency Cepstral Coefficients (MFCCs)'

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Published: 7 June 2025

Last updated: 2 August 2025

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1

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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4

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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8

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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9

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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10

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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11

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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12

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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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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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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15

Hrabina, Martin. "VÝVOJ ALGORITMŮ PRO ROZPOZNÁVÁNÍ VÝSTŘELŮ." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-409087.

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Táto práca sa zaoberá rozpoznávaním výstrelov a pridruženými problémami. Ako prvé je celá vec predstavená a rozdelená na menšie kroky. Ďalej je poskytnutý prehľad zvukových databáz, významné publikácie, akcie a súčasný stav veci spoločne s prehľadom možných aplikácií detekcie výstrelov. Druhá časť pozostáva z porovnávania príznakov pomocou rôznych metrík spoločne s porovnaním ich výkonu pri rozpoznávaní. Nasleduje porovnanie algoritmov rozpoznávania a sú uvedené nové príznaky použiteľné pri rozpoznávaní. Práca vrcholí návrhom dvojstupňového systému na rozpoznávanie výstrelov, monitorujúceho ok

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Zezula, Miroslav. "Online detekce jednoduchých příkazů v audiosignálu." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2011. http://www.nusl.cz/ntk/nusl-229484.

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This thesis describes the development of voice module, that can recognize simple speech commands by comparation of input sound with recorded templates. The first part of thesis contains a description of used algorithm and a verification of its functionality. The algorithm is based on Mel-frequency cepstral coefficients and dynamic time warping. Thereafter the hardware of voice module is designed, containing signal controller 56F805 from Freescale. The signal from microphone is conditioned by operational amplifiers and digital filter. The third part deals with the development of software for th

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Hrušovský, Enrik. "Automatická klasifikace výslovnosti hlásky R." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2018. http://www.nusl.cz/ntk/nusl-377664.

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This diploma thesis deals with automatic clasification of vowel R. Purpose of this thesis is to made program for detection of pronounciation of speech defects at vowel R in children. In thesis are processed parts as speech creation, speech therapy, dyslalia and subsequently speech signal processing and analysis methods. In the last part is designed software for automatic detection of pronounciation of vowel R. For recognition of pronounciation is used algorithm MFCC for extracting features. This features are subsequently classified by neural network to the group of correct or incorrect pronoun

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18

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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Zaheer, Ruba. "Acoustic source identification in noisy environments." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2024. https://ro.ecu.edu.au/theses/2761.

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Acoustic Source Identification (ASI) has many industrial and environmental applications, and the associated techniques and systems are continually improving in accuracy and efficiency. The detection of acoustic sources in noisy environments has been a topic of interest for researchers and scientists in the past few years. Identifying acoustic sources has been useful in many industrial and military applications, including acoustic ranging, acoustic surveillance and navigation, robot-nature interaction, and hearing aids that visualise sounds. However, it is difficult to identify a sound source w

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20

Dušil, Lubomír. "Automatické rozpoznávání logopedických vad v řečovém projevu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-218161.

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The thesis is aimed at an analysis and automatic detection of logopaedic defects in speech utterance. Its objective is to facilitate and accelerate the work of logopaedists and to increase percentage of detected logopaedic defects in children of the youngest possible age followed by the most successful treatment. It presents methods of speech work, classification of the defects within individual stages of child development and appropriate words for identification of the speech defects and their subsequent remedy. After that there are analyses of methods of calculating coefficients which reflec

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Лавриненко, Олександр Юрійович, Александр Юрьевич Лавриненко та Oleksandr Lavrynenko. "Методи підвищення ефективності семантичного кодування мовних сигналів". Thesis, Національний авіаційний університет, 2021. https://er.nau.edu.ua/handle/NAU/52212.

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Дисертаційна робота присвячена вирішенню актуальної науково-практичної проблеми в телекомунікаційних системах, а саме підвищення пропускної здатності каналу передачі семантичних мовних даних за рахунок ефективного їх кодування, тобто формулюється питання підвищення ефективності семантичного кодування, а саме – з якою мінімальною швидкістю можливо кодувати семантичні ознаки мовних сигналів із заданою ймовірністю безпомилкового їх розпізнавання? Саме на це питання буде дана відповідь у даному науковому дослідженні, що є актуальною науково-технічною задачею враховуючи зростаючу тенденцію д

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Sujatha, J. "Improved MFCC Front End Using Spectral Maxima For Noisy Speech Recognition." Thesis, 2005. https://etd.iisc.ac.in/handle/2005/1506.

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Sujatha, J. "Improved MFCC Front End Using Spectral Maxima For Noisy Speech Recognition." Thesis, 2005. http://etd.iisc.ernet.in/handle/2005/1506.

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Tang, Chu-Liang, and 唐曲亮. "Improved Mel Frequency Cepstral Coefficients Combined with Multiple Speech Features." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/57856949340151071584.

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碩士<br>國立中央大學<br>電機工程學系<br>103<br>This thesis studies the speech feature extracting and feature compensation in speech recognition. Several speech features are selected for combinations. The best one is cascading Linear Prediction Cepstral Coefficients (LPCC) and Mel-Frequency Cepstral Coefficient (MFCC). The MFCCs used here are obtained by utilizing a Gaussian Mel-Frequency band instead of using a triangular filter bank. And by experiments, it is found that the best combination ratio of LPCC and MFCC is 1:1. The thesis also showed that further improved performance is possible if Cepstral Mean

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Kuo, Yo-zhen, and 郭又禎. "Improved Mel-scale Frequency Cepstral Coefficients for Keyword Spotting Technique." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/27592493670347223949.

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碩士<br>國立中央大學<br>電機工程學系<br>102<br>In the speech recognition system, Mel frequency cepstral coefficients (MFCCs) are the feature parameters that are used widely. Because of the wide applications of MFCC in the audio signal processing, lots of studies on the improvement of MFCCs were presented. In this study, we use particle swarm optimization algorithm to optimize the weight of MFCC filter bank. We utilize the difference between voice training database’s energy statistical curve and MFCC filter bank’s envelope as fitness function. Experimental results show that the proposed MFCCs method improves

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Lin, Shih-Fen, and 林士棻. "Bird songs recognition using two-dimensional Mel-scale frequency cepstral coefficients." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/94553686394732089037.

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林士棻. "Bird songs recognition using two-dimensional Mel-scale frequency cepstral coefficients." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/38302762655714685237.

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碩士<br>中華大學<br>資訊工程學系(所)<br>94<br>We propose a method to automatically identify birds from their sounds in this paper. First, each syllable corresponding to a piece of vocalization is segmented. The average LPCC (ALPCC), average MFCC (AMFCC), Static MFCC (SMFCC), Two-dimensional MFCC (TDMFCC), Dynamic two-dimensional MFCC (DTDMFCC) and TDMFCC+DTDMFCC over all frames in a syllable are calculated as the vocalization features. Linear discriminant analysis (LDA) is exploited to increase the classification accuracy at a lower dimensional feature vector space. A clustering algorithm, called progressi

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(6642491), Jingzhao Dai. "SPARSE DISCRETE WAVELET DECOMPOSITION AND FILTER BANK TECHNIQUES FOR SPEECH RECOGNITION." Thesis, 2019.

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<p>Speech recognition is widely applied to translation from speech to related text, voice driven commands, human machine interface and so on [1]-[8]. It has been increasingly proliferated to Human’s lives in the modern age. To improve the accuracy of speech recognition, various algorithms such as artificial neural network, hidden Markov model and so on have been developed [1], [2].</p> <p>In this thesis work, the tasks of speech recognition with various classifiers are investigated. The classifiers employed include the support vector machine (SVM), k-nearest neighbors (KNN), random forest (RF

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HUANG, CHUAN-HAO, and 黃川豪. "Multi-feature Speaker Verification Based on Mel-frequency cepstral coefficients and Formants." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/4nbqev.

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Xu, Sheng-Bin, and 徐勝斌. "Continuous Birdsong Recognition Using Dynamic and Temporal Two-Dimensional Mel-Frequency Cepstral Coefficients." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/21749503795140776068.

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碩士<br>中華大學<br>資訊工程學系(所)<br>97<br>In this paper, we will propose an approach for the classification of bird species using fixed-duration sound segments extracted from continuous birdsong recordings. First, each sound segment is divided into a number of overlapped texture windows. Each texture window will be individually classified and then a fusion approach is employed to determine the classification result of the input segment. The features derived from static, transitional, and temporal information of two-dimensional Mel-frequency cepstral coefficients (TDMFCC) will be extracted for the class

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CHIANG, MING-DA, and 蔣明達. "Speaker Recognition Using Mel-Scale Frequency Cepstral Coefficients by Time Domain Filtering method." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/13444981721982290438.

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碩士<br>中華技術學院<br>電子工程研究所碩士班<br>96<br>ABSTRACT According to past papers, we find that the algorithm based on Mel-frequency cepstral coefficients (MFCCs) has a better performance than any other algorithms which based on the other feature parameters [1-7]. The Mel-frequency cepstral coefficients are taken by following procedures, including: framing, multiplied by the Hamming Window, taking the fast Fourier transform (FFT), filtered in frequency domain by Mel-frequency triangular filter bank, calculating the logarithmic energy of filter outputs, and taking discrete cosine transform (DCT) to obtain

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Chu, Feng-Seng, and 朱峰森. "Improved Approaches of Processing Perceptual Linear Prediction（PLP）and Mel Frequency Cepstrum Coefficient（MFCC）Parameters for Robust Speech Recognition." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/26578739886453071884.

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Lin, Bo-Zhi, and 林柏志. "Speaker Recognition Algorithm Using Mel-Scale Frequency Cepstral Coefficients with Two Stages Linear Prediction Filters." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/18209732501243789128.

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碩士<br>中華技術學院<br>電子工程研究所碩士班<br>94<br>The development of computer and communication technologies hastens the application requirements of speaker recognition and speech recognition. The purpose of this paper is to present a new algorithm to promote the performance of speaker recognition. The algorithm uses two stages linear prediction error filters to estimate the spectrogram of the processed speech signal. Then, the algorithm uses Mel-scale triangle bandpass filters bank to obtain the Mel-scale frequency cepstral coefficients（MFCC）to build the needed Gaussian mixture model for speaker recognitio

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Yang-Ming, Cheng, and 鄭陽銘. "A Mel-Scale Frequency Cepstral Coefficients Speaker Recognition Algorithm Based on Linear Prediction Spectrum Estimation." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/38345345070598427641.

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碩士<br>中華技術學院<br>電子工程研究所碩士班<br>93<br>According to the past research, we know that the spectrum estimation based on linear prediction is more robust than the spectrum estimation based on FFT in the case of lower SNR. In this paper, we propose a new speaker identification algorithm based on linear prediction spectrum estimation. In this algorithm, the spectrum estimation algorithm based on short time faster Furrier transform is replaced by the linear prediction spectrum estimation algorithm, then, the Mel-scale frequency cepstral coefficients are obtained by using the Mel-scale frequency triangle

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Bowman, Casady. "Perceiving Emotion in Sounds: Does Timbre Play a Role?" Thesis, 2011. http://hdl.handle.net/1969.1/ETD-TAMU-2011-12-10656.

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Acoustic features of sound such as pitch, loudness, perceived duration and timbre have been shown to be related to emotion in regard to sound, demonstrating that an important connection between the perceived emotions and their timbres is lacking. This study investigates the relationship between acoustic features of sound and emotion in regard to timbre. In two experiments we investigated whether particular acoustic components of sound can predict timbre, and particular categories of emotion, and how these attributes are related. Two behavioral experiments related perceived emotion ratings with

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Wu, Sunrise, and 吳尚叡. "Design Time Domain Filter Banks Using Least Squares Method to Calculate the Mel-Frequency Cepstral Coefficients for Speaker Recognition." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/08178129842426697899.

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碩士<br>中華技術學院<br>電子工程研究所碩士班<br>96<br>Up to now, the best speaker recognition technique is based on Mel-frequency cepstral coefficients (MFCCs) [1-4,11] method. The main procedures on taking MFCCs are undergone by: framing, Hamming windowing, multiplied by FFT（Fast Fourier Transform）[7], filtered by Mel-scale triangular filter bank, taken the logarithmic energies of outputs, and transformed by DCT (Discrete Cosine Transform)[1-8]. After these processes, the MFCCs are obtained. The main topic of this thesis is we replace previous procedures of FFT [7] and filtering using a frequency-domain Mel-sc

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Yuan, Hor, and 原禾. "Design Time Domain Filter Banks Using Least Squares Method to Calculate the Mel-Frequency Cepstral Coefficients for Non-Continuous Speech Recognition." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/76162451347630250736.

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碩士<br>中華技術學院<br>電子工程研究所碩士班<br>97<br>In speech recognition, the Mel frequency cepstral coefficients (MFCC) is currently popular to be used in speech recognition and speaker recognition[2,8-11,14,15]. To obtain the MFCC, the main procedures are filtering the speech signal by a set of triangular Mel-scale Filter Bank in the frequency domain to obtain the logarithm of the output powers of filter bank, and then taking Discrete Cosine Transform to obtain the MFCC. In this paper, the frequency domain triangular Mel-scale filter bank is replaced by a new designed time domain triangular Mel-scale filte

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