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Acoustic signal classification
Pospíšil, Aleš ; Balík, Miroslav (referee) ; Atassi, Hicham (advisor)
Bachelor's thesis is focused on automatic music genre classication. First part of work evaluates present situation and refer to published studies. Gained knowledge from there is applied in this work. In terms of nding solution for problem the work summarize and describe suitable music features and classication techniques like neural networks and k-nearest neighbor. Four selected classication classes were classical, electro, jazz and rock music. Result of work is user-friendly system that provides automatic music genre recognition. Achieved classication performance is more less comparable to human music genres recognition.
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Retinal blood vessel segmentation in fundus images via statistical-based methods
Šolc, Radek ; Walek, Petr (referee) ; Odstrčilík, Jan (advisor)
This diploma thesis deals with segmentation of blood vessel from images acquired by fundus camera. The characteristic of fundus images and current methods of segmentation are described in theoretical part. The reach of the practical part is method using statistical model. The model using Student´s distribution for automatic segmentation is gradually drafted. Firstly EM- algorithm has been incorporated and model drafted on Markov random fields for improving robustness to noise after that. Contrast of thin blood vessel is improved in image preprocessing part by discrete wave transformation. The output image is used as mask for grayscale intensity decrease of thinnest blood-vessel and intensity increase of background. Whole model was programed in Matlab. The model was tested on whole HRF database. The quantitative evaluation of binary images were compared with golden standard images.
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Emotional States of Humans and their Determination using Speech Record Analysis
Lněnička, Jakub ; Míča, Ivan (referee) ; Smékal, Zdeněk (advisor)
The aim of the diploma project is to find a method through which it will be possibleto classify the selected emotion from speech. At the beginning of the work deals with the description of the human body and their voice-generating operation. Furthermore, the text deals with the problem of the human voice into digital form.Great attention is paid to the parameters of the speech signal with an emphasis on describing the symptoms to help the selected emotion. The work deals with therecognition of emotions and a description of some of them. The main part is finding the best methods to reduce symptoms of segmental and suprasegmental speech utterances. The results of success was achieved by comparing the classification of selected emotions when using multiple methods and compare their results. The most important criterion in assessing the results ofthe reduction parameters of the speech signal, based on previous research in this area.
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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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Emotional State Recognition Based on Speech Signal Analysis
Čermák, Jan ; Atassi, Hicham (referee) ; Smékal, Zdeněk (advisor)
The thesis is focused on the emotional states classification in the Matlab program, using neural networks and the classifier which is based on a combination of Gaussian density functions. It deals with the speech signal processing; the prosodic and spectral signs and the MFCC coefficients were extracted from the signal. The work also deals with the quality evaluation of individual signs of which the most suitable were chosen in order to provide the correct classification of emotional states. In order to identify the emotional states, two different methods were used. The first method of classification was the use of neural networks with differently selected parameters, and the second method was the use of the Gaussian mixture model (GMM). In both methods, a database of emotional utterances was divided into the training group and the test group. The testing was based on a method independent of the speaker. The work also includes the comparison of individual analyzed methods as well as the representation and comparison of the results. The conclusion comprises a proposition for the best parameters and the best classifier for the recognition of the speaker’s emotional state.
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Automatic vocal-oriented recognition of human emotions
Houdek, Miroslav ; Přinosil, Jiří (referee) ; Atassi, Hicham (advisor)
This master thesis concerns with emotional states and gender recognition on the basis of speech signal analysis. We used various prosodic and cepstral features for the description of the speech signal. In the text we describe non-invasive methods for glottal pulses estimation. The described features of speech were implemented in MATLAB. For their classification we used the GMM classifier, which uses the Gaussian probability distribution for modeling a feature space. Furthermore, we constructed a system for recognition of emotional states of the speaker and a system for gender recognition from speech. We tested the success of created systems with several features on speech signal segments of various lengths and compared the results. In the last part we tested the influence of speaker and gender on the success of emotional states recognition.
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Neural networks in speaker classification
Svoboda, Libor ; Atassi, Hicham (referee) ; Míča, Ivan (advisor)
The content of this work is focused on the neural network per speaker recognition. The work deals with problems of processing speech signal and there are introduction some types of neural network. The part of work was made database of records from speakers with have various sex and ages. The train and test group was made from the database. For classifier were suggested afterwards. One of them was nominated on base Gaussian mixture model and three of them were nominated on neural. This system was tested and analyzed on the basis of age, gender and both criterions each other at the end. Attention is focused on choice suitable feature in each mission of classification at the same time. At the end of work are introduced results of analysis for individual groups and features. The most suitable features are diagnosed from given mission of classification and the most prosperous classifier.
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Empirical testing of hybrid NKPC for the Czech Republic and the Slovakia
Řehůřek, Tomáš ; Slaný, Martin (advisor) ; Janíčko, Martin (referee)
This thesis testing hybrid NKPC for the Czech Republic and the Slovakia. Analysis made by Generalized Method of Moments (GMM) and complementary Two Stage Least Squares (TSLS) demonstrated that given concept, representing short-run relationship between inflation, inflation expectations and marginal cost/output gap, is relevant for the Slovakia and irrelevant for the Czech Republic. And NKPC is relevant for both countries. Thesis also shows the development of the Phillips curve (from its original version up to the modern version) and its derivation. In this thesis is also introduced so called the triangle model. This thesis also presents several (similar) researches which testing (hybrid) NKPC and their results are compared with the results of this thesis.
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Rozuzlení vztahu korupce a šedé ekonomiky
Rais, Jonáš ; Rod, Aleš (advisor) ; Potužák, Pavel (referee)
This paper analyses the relationship between corruption and shadow economy. Synthesis of existing theoretical models and the empirical findings in this paper show that different types of corrupt behavior interact with shadow economy differently. Therefore, the relationship between contexts differs. Overall, it seems that the mechanisms leading to the complementary relationship are more prevalent. The results also imply that the quality of public goods available in the official economy influences where the different types of corrupt behavior manifest. When the quality of the public goods is high, corruption and shadow economy appear to be substitutes and when the quality is low, they are complements. However, the relationship is not robust and depends upon the measures used to assess the public goods quality. Furthermore, corruption and shadow economy seem to be complements in decentralized countries and countries with high taxes.
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Empirical Testing of the New Keynesian Phillips Curve in the Czech Republic
Plašil, Miroslav ; Arlt, Josef (advisor) ; Pánková, Václava (referee) ; Komárek, Luboš (referee)
New keynesian Phillips curve (NKPC) has become a central model to study the relation between inflation and real economic activity, notably in the framework of optimal monetary policy design. However, some recent evidence suggests that empirical data are usually at odds with the underlying theory. The model due to its inherent structure represents a statistical challenge in its own right. Since Galí and Gertler (1999) published their seminal paper introducing estimation via GMM techniques, they have triggered a heated debate on its empirical relevance. Their approach has been heavily criticised by later authors, mainly on the grounds of questionable behaviour of GMM estimator in the NKPC context and/or its small sample properties. The common criticism includes sensitivity to the choice of instrument set, weak identification and small sample bias. In this thesis I propose a new estimation strategy that provides a remedy to above mentioned shortcomings and allows to obtain reliable estimates. The procedure exploits recent advances in GMM theory as well as in other fields of statistics, in particular in the area of time series factor analysis and bootstrap. The proposed estimation strategy consists of several consecutive steps: first, to reduce a small sample bias resulting from excessive use of instruments I summarize all available information by employing factor analysis and include estimated factors into information set. In the second step I use statistical information criteria to select optimal instruments and eventually I obtain confidence intervals on parameters using bootstrap method. In NKPC context all these methods were used for the first time and can also be used independently. Their combination however provides synergistic effect that helps to improve the properties of estimates and to check the efficiency of given steps. Obtained results suggest that NKPC model can explain Czech inflation dynamics fairly well and provide some support for underlying theory. Among other things the results imply that the policy of disinflation may not be as costly with respect to a loss in aggregate product as earlier versions of Phillips curve would indicate. However, finding a good proxy for real economic activity has proved to be a difficult task. In particular we demonstrated that results are conditional on how the measure is calculated, some measures even showed countercyclical behaviour. This issue -- in the thesis discussed only in passing -- is a subject of future research. In addition to the proposed strategy and provided parameter estimates the thesis brings some partial simulation-based findings. Simulations elaborate on earlier literature on naive bootstrap in GMM context and study performance of bootstrap modifications of unit root and KPSS test.
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