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Chord recognition
Soudek, Ondřej ; Glembek, Ondřej (referee) ; Černocký, Jan (advisor)
The goal of this bachelor thesis is to create an application, that will perform a real-time analysis of a signal from electric or acoustic guitar and will recognize a chord or tone, played by user. This document contains basics of music theory and the solution of the problem, as well as the results of testing.
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Object Recognition by Means of Evolutionary Methods
Lýsek, Jiří ; Škorpil, Vladislav (referee) ; Rábová, Ivana (referee) ; Šťastný, Jiří (advisor)
This thesis deals with usage of evolutionary methods, grammatical evolution particularly in application for object recognition in an image. Basic principles of object recognition and evolutionary methods with focus on grammatical evolution are described. The core of the thesis lies in design of techniques and methods for classifier programs creation using grammatical evolution. Also the designed fitness formula is presented. In the end, created testing and development environment in Java programming language is described.
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Automatic vocal-oriented recognition of human emotions
Adamský, Aleš ; Kubánková, Anna (referee) ; Atassi, Hicham (advisor)
This work deals with the characteristics, formation, representation and analysis of speech and the speech signal. It explains the details of concepts such as emotions and prosody. It analyzes different emotion state of human, as well as prosodic parameters: intensity, harmonisity and formants. It includes a database emotional states of human. This database was analyzed using the Praat and suitable features were selected for detecting of emotional states of human speech. It also deals with neural networks. It contains a description of Java Neural Network Simulator, which is used to detect emotional states of human speech. Results of recognition are processed in tables and graphs for easier navigation.
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Multilingual analysis of human emotional states
Rendek, Tomáš ; Koula, Ivan (referee) ; Atassi, Hicham (advisor)
This work deals with the properties of the speech signal. At the beginning it introduces a process of generation of the speech. Then, it covers the prosodic features of the speech, which represent a related characteristic of emotions. It defines an emotion itself, as well as the basic features and parameters of the human speech. For the analysis we use the program called Praat. As it is an unknown program, we devote a part of the work to it, which acquaints us with its advantages. The next part of this paper comprises also two enclosed databases containing records of particular emotional states of human. These databases were created and collected for Slovak and German language. However, none of them contain spontaneous material. Next, the work concerns a concept of the neural networks. It regards it as a possible realization of recognizing of emotional characteristics. The initial analysis presents large number of gained features, out of which only the best twelve were selected on the basis of geometric separability. These features are distinct for both sexes, as well as for both nationalities. Consequently, they are used for training with a neural network. The work concludes by summarizing of the results discussing the successfulness with recognition of emotional states. It also gives possible reasons which lead to degradation of their successful classifying. The thesis contains a CD with all the partial and ultimate results, and files with records for Slovak and German language.
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Pattern Recognition in Surface Electromyography
Chalupa, D.
In this paper, a method is proposed to differentiate patterns in one-channel surface electromyography. This particular method uses an example signal containing all relevant patterns to learn spectral coefficients. These coefficients are used to analyze unknown signals. An example of finger recognition algorithm based on a signal from forearm muscles is shown and discussed.
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Automatic image annotation
Hegmon, Jiří ; Karásek, Jan (referee) ; Burget, Radim (advisor)
Recognition and comparison of image is one of the main problems and area of the field of computer vision. This thesis adds to these two issues the third, the recognition image semantics, so called annotations or labels. This work uses the knowledge of methods of recognizing the similarity of images to create a tool that is able based on training dataset of images and annotations, create a group most likely annotation for the test set of images. This work presents several types of test datasets suitable for the detection of annotation information for images. Subsequently, best set with the necessary training dataset size and enough information about annotations is selected. Based on this training dataset algorithm is designed for easy loading test set without large demands on computer performance. Evaluation of annotation information is done based on different similarity algorithms. At the beginning of this work was to use a simple, but not very effective method of MSE and comparison of color histograms, but gradually it was necessary to move to using more advanced methods (such as Tamura, Gabor, CEDD nebo různé druhy hostistogramů). The results of this comparison are then taken to evaluate the likelihood of the annotation for the image specified test set. The last part is an evaluation of the accuracy of annotation based on information from the test set.
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Artificial neural network RCE
Maceček, Aleš ; Klusáček, Jan (referee) ; Jirsík, Václav (advisor)
This paper is focused on an artificial neural network RCE, especially describing the topology, properties and learning algorithm of the network. This paper describes program uTeachRCE developed for learning the RCE network and program RCEin3D, which is created to visualize the RCE network in 3D space. The RCE network is compared with a multilayer neural network with a learning algorithm backpropagation in the practical application of recognition letters. For a descriptions of the letters were chosen moments invariant to rotation, translation and scaling image.
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Face detection and recognition methods
Zbranek, Miroslav ; Horák, Karel (referee) ; Honec, Peter (advisor)
The aim of this diploma thesis is to explore methods of face detection and recognition in the picture. The method for face detection and the method for face recognition will be chosen according to literature survey. Both methods will be implemented using the OpenCV library and a program language C/C++. The result of this project is creation of graphic interface which use programmed function for face detection and recognition from a picture and also a camcorder.
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Real-time Facial Feature Tracking
Peloušek, Jan ; Mekyska, Jiří (referee) ; Přinosil, Jiří (advisor)
This thesis considers the problematic of the object recognition in a digital picture, particularly about the human face recognition and its components. There are described the basics of the computer vision, the object detector Viola-Jones, its computer realization with help of the OpenCV libraries and the test results. This thesis also describes the accurate system of the facial features detection per the algorithm of the Active Shape Models and also related mechanism of the classifier training, including the software implementation.
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Paralinguistic signals recognition in spoken dialogs
Mašek, Jan ; Míča, Ivan (referee) ; Atassi, Hicham (advisor)
This document describes the three methods for the detection and classification of paralinguistic expressions such as laughing and crying from usual speech by analysis of the audio signal. The database of records was originally designed for this purpose. When analyzing everyday dialogs, music might be included, so the database was extended by four new classes as speech, music, singing with music and usual speech with background music. Feature extraction, feature reduction and classification are common steps in recognizing for all three methods. Difference of the methods is given by classification process in detail. One classification of all six classes at once is proposed in the first method called straight approach. In the second method called decision tree oriented approach we are using five intuitive sub classifiers in the tree structure and the final method uses for classification emotion coupling approach. The best features were reduced by feature evaluation using F-ratio and GMM classifiers were used for the each classification part.
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