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Acoustic analysis of poem recitation in patients with Parkinson's disease
Mucha, Ján ; Smékal, Zdeněk (referee) ; Galáž, Zoltán (advisor)
Diploma thesis is focused on the acoustic analysis of poetry recitation in patients with Parkinson's disease. This disease is associated with speech disorder called hypokinetic dysarthria. One objective of this thesis was familiarization with process, symptoms and treatment of these diseases. In thesis is described preprocessing and parametrization of the speech signal and the binary classification methods. Subsequently, it is the above proposal modular system of auto-diagnosis of Parkinson's disease based on acoustic analysis of the speech. The proposed system is implemented in MATLAB. Classification of calculated parameters is realized using the method of Random forest and Support vector machine. The results of these methods are compared and listed in the thesis. The main objective and the result of this thesis is a system of automatic diagnosis of Parkinson's disease based on acoustic analysis of the poem recitation.
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Speech analysis using iOS or Android system
Hejda, Jakub ; Galáž, Zoltán (referee) ; Mekyska, Jiří (advisor)
The telemendicine is rapidly growing industrial branch and gives an opportunity of easier diagnosis needed for much more effective treating methods development. The aim is to develop a smartphone application for the diagnosis of prolonged phonation of pati- ents with Parkinson’s disease. Qt has been chosen s the main framework, allowing the multiplatform development using various combination of languages consisting of C++, QML, Java and JavaScript. Required functionality has been completely implemented. The application guides a user through the process of recording, then executes the analy- sis, saves data into database and displays the history into well-arranged charts, the user can also set a notification to remind him of a recording. The application can be compiled for all widely used mobile and desktop systems.
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Research of tempo features comparing audio records
Ištvánek, Matěj ; Galáž, Zoltán (referee) ; Kiska, Tomáš (advisor)
This thesis deals with technical properties of audio signals or more precisely of recordings from the prepared database and describes parameters which are used for music transcription and analysis of audio signals. It summarises information about music theory and automatic transcription of audio recordings, introduces specialist studies that deal with problems of signal analysis and their results. Furthermore it mentions attributes with the best ability to generally differentiate included songs from rhythmical and metrical aspects. Thesis analyses in the MATLAB language, from the prepared database, all interpretations of the piece "String Quartet No. 1 – IV. Con moto" from Leoš Janáček with two selected methods and shows results of the analysis and comparing of the methods. Finally the work summarizes all information and problems of the thesis.
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Twitter data analysis tool
Rýdl, Pavel ; Komosný, Dan (referee) ; Galáž, Zoltán (advisor)
This work deals with the creation of an application for automatic downloading and Twitter data analysis based on natural language processing techniques. The application is created in the Python programming language. A development environment Jupyter Notebook was used for creating the application, where the entire application, including GUI, was implemented. In the section of theory are data downloading issues and data analysis by natural language processing described. In the part of implementation there is solution of the application described in several steps, such as creating the application on the Twitter's side, downloading, preprocessing, data analysis with techniques of natural language processing and following visualization. There was also a technique with no natural language processing implemented. Testing run on tweets that contained reference to US president Donald Trump.
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Tool for parsing and analysing of web pages
Odstrčil, Štěpán ; Ilgner, Petr (referee) ; Galáž, Zoltán (advisor)
This bachelor’s thesis is dealing with parsing of text in HTML pages and its analysis. Practices from Natural Language Processing were used. There were written libraries (or packages) in programming language Python, with use of modern practices, techniques and libraries. The usages and examples of these libraries and classes were made. All these libraries were tested using Unit tests. Application contains GUI (Graphical User Interface) for wasier usefulness and demonstration of functionality.
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Audio noise reduction using deep neural networks
Talár, Ondřej ; Galáž, Zoltán (referee) ; Harár, Pavol (advisor)
The thesis focuses on the use of deep recurrent neural network, architecture Long Short-Term Memory for robust denoising of audio signal. LSTM is currently very attractive due to its characteristics to remember previous weights, or edit them not only according to the used algorithms, but also by examining changes in neighboring cells. The work describes the selection of the initial dataset and used noise along with the creation of optimal test data. For network training, the KERAS framework for Python is selected. Candidate networks for possible solutions are explored and described, followed by several experiments to determine the true behavior of the neural network.
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Application of statistical analysis of speech in patients with Parkinson's disease
Bijota, Jan ; Mžourek, Zdeněk (referee) ; Galáž, Zoltán (advisor)
This thesis deals with speech analysis of people who suffer from Parkinson’s disease. Purpose of this thesis is to obtain statistical sample of speech parameters which helps to determine if examined person is suffering from Parkinson’s disease. Statistical sample is based on hypokinetic dysarthria detection. For speech signal pre-processing DC-offset removal and pre-emphasis are used. The next step is to divide signal into frames. Phonation parameters, MFCC and PLP coefficients are used for characterization of framed speech signal. After parametrization the speech signal can be analyzed by statistical methods. For statistical analysis in this thesis Spearman’s and Pearson’s correlation coefficients, mutual information, Mann-Whitney U test and Student’s t-test are used. The thesis results are the groups of speech parameters for individual long czech vowels which are the best indicator of the difference between healthy person and patient suffering from Parkinson’s disease. These result can be helpful in medical diagnosis of a patient.
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Library for Python used for dysarthric speech parameterization
Koutný, Tomáš ; Galáž, Zoltán (referee) ; Mucha, Ján (advisor)
Bachelor thesis is focused on parameterization of dysartoric speech. Attention is paid to methods of Speech Signal Analysis for Parkinson's disease, modern parametrization techniques, which are designed to quantify the damage of motoric aspects of speech and implementation of selected parameters in Python. The main goal of this work was to create a parameter library that is realized in the PyCharm development environment.
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Django framework based web application for objective analysis of hypokinetic dysarthria
Čapek, Karel ; Zvončák, Vojtěch (referee) ; Galáž, Zoltán (advisor)
This master´s thesis deals with the calculation of parameters that would be able to differentiate healthy speech and speech impaired by hypokinetic dysarthria. There was staged hypokinetic dysarthria, which is a motoric disorder of speech and vocal tract. Were studied speech signal processing methods. Further parameters were studied, which could well differentiate healthy and diseased speech. Subsequently, these parameters were programmed in Python programming language. The next step was to create a web application in Django framework, which is used for the analysis of the dyzartic speech.
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