Global ETD Search

61	Rozpoznávání a klasifikace emocí na základě analýzy řeči / Emotional State Recognition and Classification Based on Speech Signal Analysis Černý, Lukáš January 2010 (has links) The diploma thesis focuses on classification of emotions. Thesis deals about parameterization of sounds files by suprasegment and segment methods with regard for next used of these methods. Berlin database is used. This database includes many of sounds records with emotions. Parameterization creates files, which are divided to two parts. First part is used for training and second part is used for testing. Point of interest is self-organization network. Thesis includes Matlab´s program which can be used for parameterization of any database. Data are classified by self-organization network after parameterization. Results of hits rates are presented at the end of this diploma thesis.
62	Analýza Parkinsonovy nemoci pomocí segmentálních řečových příznaků / Analysis of Parkinson's disease using segmental speech parameters Mračko, Peter January 2015 (has links) This project describes design of the system for diagnosis Parkinson’s disease based on speech. Parkinson’s disease is a neurodegenerative disorder of the central nervous system. One of the symptoms of this disease is disability of motor aspects of speech, called hypokinetic dysarthria. Design of the system in this work is based on the best known segmental features such as coefficients LPC, PLP, MFCC, LPCC but also less known such as CMS, ACW and MSC. From speech records of patients affected by Parkinson’s disease and also healthy controls are calculated these coefficients, further is performed a selection process and subsequent classification. The best result, which was obtained in this project reached classification accuracy 77,19%, sensitivity 74,69% and specificity 78,95%.
63	Utilisation des signaux du cerveau (EEG) et vocaux pour la détection et le monitoring des facultés d'une personne Ben Messaoud, Aymen January 2020 (has links) (PDF) No description available. Alcool Auto Automobile Cannabis Cerveau Classification Communication Conduite Convolutif Convolutionnel Détection Dysarthrie EEG Électroencéphalographie Élocution Faculté FFT Français Humain LPC LPCC MEL MFCC Parole PLP Signal Son Véhicule Vocal Voix Voyelle
64	Fixed-point implementace rozpoznávače řeči / Fixed-Point Implementation Speech Recognizer Král, Tomáš January 2007 (has links) Master thesis is related to the problematics of automatic speech recognition on systems with restricted hardware resources - embedded systems. The object of this work was to design and implement speech recognition system on embedded systems, that do not contain floating-point processing units. First objective was to choose proper hardware architecture. Based on the knowledge of available HW resources, the recognition system design was made. During the system development, optimalization was made on constituent elements so they could be mounted on chosen HW. The result of the the project is successful recognition of Czech numerals on embedded system.
65	Biometric Multi-modal User Authentication System based on Ensemble Classifier Assaad, Firas Souhail January 2014 (has links) No description available. Bioinformatics Computer Engineering Computer Science Artificial Intelligence algorithm authentication biometric classifier credentials eigenfaces multi-modal recognition score level fusion MFCC Viola Jones LBG Mel Frequency Cepstral Coefficients voice print Linde Buzo Gray ELSDSR Yale Extended NIST FERET Server client
66	Detekce logopedických vad v řeči / Detection of Logopaedic Defects in Speech Pešek, Milan January 2009 (has links) 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 the next part of the thesis there are results of testing of selected methods presented. For logopaedia speech defects recognition algorithms are used in order to extract the features MFCC and PLP. The segmentation of words to speech sounds is performed on the base of Differential Function method. The extracted features of a sound are classified into either a correct or an incorrect pronunciation class with one of tested methods of pattern recognition. To classify the features, the k-NN, SVN, ANN, and GMM methods are tested.
67	Automatic Speech Quality Assessment in Unified Communication : A Case Study / Automatisk utvärdering av samtalskvalitet inom integrerad kommunikation : en fallstudie Larsson Alm, Kevin January 2019 (has links) Speech as a medium for communication has always been important in its ability to convey our ideas, personality and emotions. It is therefore not strange that Quality of Experience (QoE) becomes central to any business relying on voice communication. Using Unified Communication (UC) systems, users can communicate with each other in several ways using many different devices, making QoE an important aspect for such systems. For this thesis, automatic methods for assessing speech quality of the voice calls in Briteback’s UC application is studied, including a comparison of the researched methods. Three methods all using a Gaussian Mixture Model (GMM) as a regressor, paired with extraction of Human Factor Cepstral Coefficients (HFCC), Gammatone Frequency Cepstral Coefficients (GFCC) and Modified Mel Frequency Cepstrum Coefficients (MMFCC) features respectively is studied. The method based on HFCC feature extraction shows better performance in general compared to the two other methods, but all methods show comparatively low performance compared to literature. This most likely stems from implementation errors, showing the difference between theory and practice in the literature, together with the lack of reference implementations. Further work with practical aspects in mind, such as reference implementations or verification tools can make the field more popular and increase its use in the real world. speech voice communication qoe quality of experience unified communication uc speech quality assessment speech quality voice calls gaussian mixture model gmm gaussian mixture regression gmr mel frequency cepstrum coefficients mfcc human feature cepstrum coefficients hfcc gfcc Software Engineering Programvaruteknik
68	A Hybrid Approach to Music Recommendation: Exploiting Collaborative Music Tags and Acoustic Features Kaufman, Jaime C. 01 January 2014 (has links) Recommendation systems make it easier for an individual to navigate through large datasets by recommending information relevant to the user. Companies such as Facebook, LinkedIn, Twitter, Netflix, Amazon, Pandora, and others utilize these types of systems in order to increase revenue by providing personalized recommendations. Recommendation systems generally use one of the two techniques: collaborative filtering (i.e., collective intelligence) and content-based filtering. Systems using collaborative filtering recommend items based on a community of users, their preferences, and their browsing or shopping behavior. Examples include Netflix, Amazon shopping, and Last.fm. This approach has been proven effective due to increased popularity, and its accuracy improves as its pool of users expands. However, the weakness with this approach is the Cold Start problem. It is difficult to recommend items that are either brand new or have no user activity. Systems that use content-based filtering recommend items based on extracted information from the actual content. A popular example of this approach is Pandora Internet Radio. This approach overcomes the Cold Start problem. However, the main issue with this approach is its heavy demand on computational power. Also, the semantic meaning of an item may not be taken into account when producing recommendations. In this thesis, a hybrid approach is proposed by utilizing the strengths of both collaborative and content-based filtering techniques. As proof-of-concept, a hybrid music recommendation system was developed and evaluated by users. The results show that this system effectively tackles the Cold Start problem and provides more variation on what is recommended. Thesis University of North Florida UNF music collaborative filtering collective intelligence content-based filtering MFCC recommend information retrieval database Pandora Last.fm music recommendation system UNF Databases and Information Systems Software Engineering Theory and Algorithms
69	Robot s autonomním audio-vizuálním řízením / Robot with autonomous audio-video control Dvořáček, Štěpán January 2019 (has links) This thesis describes the design and realization of a mobile robot with autonomous audio-visual control. This robot is able of movement based on sensors consisting of camera and microphone. The mechanical part consists of components made with 3D print technology and omnidirectional Mecanum wheels. Software utilizes OpenCV library for image processing and computes MFCC a DTW for voice command detection.
70	Rozpoznáváni standardních PILOT-CONTROLLER řídicích povelů v hlasové podobě / Voice recognition of standard PILOT-CONTROLLER control commands Kufa, Tomáš January 2009 (has links) 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 usage propriety of both methods is verified by practical testing and results evaluation.

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