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Emotion Recognition from Acted and Spontaneous Speech
Atassi, Hicham ; Přibil, Jiří (referee) ; Zahradník, Pavel (referee) ; Smékal, Zdeněk (advisor)
Dizertační práce se zabývá rozpoznáním emočního stavu mluvčích z řečového signálu. Práce je rozdělena do dvou hlavních častí, první část popisuju navržené metody pro rozpoznání emočního stavu z hraných databází. V rámci této části jsou představeny výsledky rozpoznání použitím dvou různých databází s různými jazyky. Hlavními přínosy této části je detailní analýza rozsáhlé škály různých příznaků získaných z řečového signálu, návrh nových klasifikačních architektur jako je například „emoční párování“ a návrh nové metody pro mapování diskrétních emočních stavů do dvou dimenzionálního prostoru. Druhá část se zabývá rozpoznáním emočních stavů z databáze spontánní řeči, která byla získána ze záznamů hovorů z reálných call center. Poznatky z analýzy a návrhu metod rozpoznání z hrané řeči byly využity pro návrh nového systému pro rozpoznání sedmi spontánních emočních stavů. Jádrem navrženého přístupu je komplexní klasifikační architektura založena na fúzi různých systémů. Práce se dále zabývá vlivem emočního stavu mluvčího na úspěšnosti rozpoznání pohlaví a návrhem systému pro automatickou detekci úspěšných hovorů v call centrech na základě analýzy parametrů dialogu mezi účastníky telefonních hovorů.
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The relation of emotions and intonation curves
Gavlasová, Radka ; Smékal, Zdeněk (referee) ; Tučková,, Jana (advisor)
This thesis deals with intonation curves and their relation to human emotions. Besides the theoretical part where you can learn about speech production, signal processing and psychological distribution of emotions, there is also a unique database recorded with the help of two professional actors. The main goal of this thesis is to classify created data using artificial neural networks into four classes. Those classes are anger, joy, boredom and sadness. The practical part was implemented in a programming platform called Matlab using Classification Learner app. Features used for this method were variations of fundamental frequency and MFCC. The results were compared with a listening survey so that it could be determined whether the results provided by neural network are relevant to some kind of a human factor. Success rate of the trained models reached 82 %, new data testing reached 75 %. Listening survey confirmed that the results correspond to the assumption of human perception. Better success rate would be accomplished by using a bigger set of higher quality data.
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Detection of face parts in the thermographic spectrum
Šujan, Miroslav ; Smékal, Zdeněk (referee) ; Mekyska, Jiří (advisor)
Master´s thesis deals with current problems of face detection and its parts in the infrared thermographic spectrum. Most previously published literature deals with the detection in the visible spectrum, making the thermographic detection range an interesting alternative. The work deals with the processing of image signals, images and faces in thermographic spectrum, selected methods of face detection and its parts and also deals with practical system design for detecting facial parts in this spectrum and its subsequent testing.
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Creating 3D Model of Temporomandibular Joint
Šmirg, Ondřej ; Bartušek, Karel (referee) ; Liberda,, Ondřej (referee) ; Smékal, Zdeněk (advisor)
The dissertation thesis deals with 3D reconstruction of the temporomandibular joint from 2D slices of tissue obtained by magnetic resonance. The current practice uses 2D MRI slices in diagnosing. 3D models have many advantages for the diagnosis, which are based on the knowledge of spatial information. Contemporary medicine uses 3D models of tissues, but with the temporomandibular joint tissues there is a problem with segmenting the articular disc. This small tissue, which has a low contrast and very similar statistical characteristics to its neighborhood, is very complicated to segment. For the segmentation of the articular disk new methods were developed based on the knowledge of the anatomy of the joint area of the disk and on the genetic-algorithm-based statistics. A set of 2D slices has different resolutions in the x-, y- and z-axes. An up-sampling algorithm, which seeks to preserve the shape properties of the tissue was developed to unify the resolutions in the axes. In the last phase of creating 3D models standard methods were used, but these methods for smoothing and decimating have different settings (number of polygons in the model, the number of iterations of the algorithm). As the aim of this thesis is to obtain the most precise model possible of the real tissue, it was necessary to establish an objective method by which it would be possible to set the algorithms so as to achieve the best compromise between the distortion and the model credibility achieve.
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Echo suppressor
Kratochvíl, Pavel ; Smékal, Zdeněk (referee) ; Sysel, Petr (advisor)
In the communication networks a common problem is a returned copy of an original signal that goes back through the network to the sender, disturbing the communication. This problem is attacked using Echo Cancellors that should comply with the specifications of ITU-T organization. Algorithms suitable for cancellors should be tested under specified conditions, then a decision should be taken regarding their deployment. This work deals with basic Least Mean Squares and Normalized Least Mean Squares algorithms.
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Software defined networks
Flimel, Peter ; Smékal, Zdeněk (referee) ; Filka, Miloslav (advisor)
This diploma work describes the software-defined network focusing on optical networks. Subsequently designed their own software network that is implemented in the environment OMNeT ++. This work deals with SDN (software-defined network), and impact on current communications environment in the world of telecom-munications services.
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Analysis and segmentation of tomographic images
Dorazil, Jan ; Bartušek, Karel (referee) ; Smékal, Zdeněk (advisor)
The thesis deals with the edge detection in the magnetic resonance images and their segmentation. There are adduced the gradient based methods, methods based on zero-crossing in the Laplacian images and also methods combined both of the methods adduced above. These methods are compared to find the best one for the temporo-mandibular joint detection. Consequently sufficient segmentation method for particular parts of the temporo-mandibular joint (the condyle, the acetabulum and the articular disk) separation is chosen.
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3D shape from MRI
Menclík, Tomáš ; Smékal, Zdeněk (referee) ; Šmirg, Ondřej (advisor)
The main aim of the thesis is the reconstruction of three-dimensional surface from a~set of two-dimensional images. For the implementation of this application the programming language Java and its extension, that allows work with three-dimensional models, were chosen. First, viewing of three-dimensional models of two different file formats was necessary to allow. To create the three-dimensional models, the Marching Cubes algorithm was used. This algorithm is decribed theoretically in the text, description of the implementation and correction of deficiencies follows. Finally, the implementation of the inversion procedure of reconstruction of the three-dimensional surface, which is the extraction of two-dimensional images from the three-dimensional model, is described.
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Multi-channel Methods of Speech Enhancement
Zitka, Adam ; Balík, Miroslav (referee) ; Smékal, Zdeněk (advisor)
This thesis deals with multi-channel methods of speech enhancement. Multichannel methods of speech enhancement use a few microphones for recording signals. From mixtures of signals, for example, individual speakers can be separated, noise should be reduced etc. with using neural networks. The task of separating speakers is known as a cocktail-party effect. The main method of solving this problem is called independent component analysis. At first there are described its theoretical foundation and presented conditions and requirements for its application. Methods of ICA try to separate the mixtures with help of searching the minimal gaussian properties of signals. For the analysis of independent components are used different mathematical properties of signals such as kurtosis and entropy. Signals, which were mixed artificially on a computer, can be relatively well separated using, for example, FastICA algorithm or ICA gradient ascent. However, difficult is situation, if we want to separate the signals created in the real recording enviroment, because the separation of speech people speaking at the same time in the real environment affects other various factors such as acoustic properties of the room, noise, delays, reflections from the walls, the position or the type of microphones, etc. Work presents aproach of independent component analysis in the frequency domain, which can successfully separate also recordings made in the real environment.
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State of the art speech features used during the Parkinson disease diagnosis
Bílý, Ondřej ; Smékal, Zdeněk (referee) ; Mekyska, Jiří (advisor)
This work deals with the diagnosis of Parkinson's disease by analyzing the speech signal. At the beginning of this work there is described speech signal production. The following is a description of the speech signal analysis, its preparation and subsequent feature extraction. Next there is described Parkinson's disease and change of the speech signal by this disability. The following describes the symptoms, which are used for the diagnosis of Parkinson's disease (FCR, VSA, VOT, etc.). Another part of the work deals with the selection and reduction symptoms using the learning algorithms (SVM, ANN, k-NN) and their subsequent evaluation. In the last part of the thesis is described a program to count symptoms. Further is described selection and the end evaluated all the result.
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