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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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Relationship between Electrophysiological Activity and Dynamic Functional Connectivity of Large-scale Brain Networks in fMRI Data
Lamoš, Martin ; Hlinka, Jaroslav (referee) ; Kremláček, Jan (referee) ; Jan, Jiří (advisor)
Functional brain connectivity is a marker of the brain state. Growing interest in the examination of large-scale brain network functional connectivity dynamics is accompanied by an effort to find the electrophysiological correlates. The commonly used constraints applied to spatial and spectral domains during EEG data analysis may leave part of the neural activity unrecognized. A proposed approach blindly reveals multimodal EEG spectral patterns that are related to the dynamics of the BOLD functional network connectivity. The blind decomposition of EEG spectrogram by Parallel Factor Analysis has been shown to be a useful technique for uncovering patterns of neural activity where each pattern contains three signatures (spatial, temporal, and spectral). The decomposition takes into account the trilinear structure of EEG data, as compared to the standard approaches of electrode averaging, electrode subset selection or using standard frequency bands. The simultaneously acquired BOLD fMRI data were decomposed by Independent Component Analysis. Dynamic functional connectivity was computed on the component’s time series using a sliding window correlation, and functional connectivity network states were then defined based on the values of the correlation coefficients. ANOVA tests were performed to assess the relationships between the dynamics of functional connectivity network states and the fluctuations of EEG spectral patterns. Three patterns related to the dynamics of functional connectivity network states were found. Previous findings revealed a relationship between EEG spectral pattern fluctuations and the hemodynamics of large-scale brain networks. This work suggests that the relationship also exists at the level of functional connectivity dynamics among large-scale brain networks when no standard spatial and spectral constraints are applied on the EEG data.
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Time Frequency Analysis of ERP Signals
Bartůšek, Jan ; Provazník, Ivo (referee) ; Černocký, Jan (advisor)
Tato práce se zabývá vylepšením algoritmu pro sdružování (clustering) ERP signálů pomocí analýzy časových a prostorových vlastností pseudo-signálů získaných za pomocí metody analýzy nezávislých komponent (Independent Component Analysis). Naším zájmem je nalezení nových vlastností, které by zlepšily stávající výsledky. Tato práce se zabývá použitím Fourierovy transformace (Fourier Transform), FIR filtru a krátkodobé Fourierovy transformace ke zkvalitnění informace pro sdružovací algoritmy. Princip a použitelnost metody jsou popsány a demonstrovány ukázkovým algoritmem. Výsledky ukázaly, že pomocí dané metody je možné získat ze vstupních dat zajímavé informace, které mohou být úspěšně použity ke zlepšení výsledků.
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Underdetermined Blind Audio Signal Separation
Čermák, Jan ; Smékal, Zdeněk (advisor)
We often have to face the fact that several signals are mixed together in unknown environment. The signals must be first extracted from the mixture in order to interpret them correctly. This problem is in signal processing society called blind source separation. This dissertation thesis deals with multi-channel separation of audio signals in real environment, when the source signals outnumber the sensors. An introduction to blind source separation is presented in the first part of the thesis. The present state of separation methods is then analyzed. Based on this knowledge, the separation systems implementing fuzzy time-frequency mask are introduced. However these methods are still introducing nonlinear changes in the signal spectra, which can yield in musical noise. In order to reduce musical noise, novel methods combining time-frequency binary masking and beamforming are introduced. The new separation system performs linear spatial filtering even if the source signals outnumber the sensors. Finally, the separation systems are evaluated by objective and subjective tests in the last part of the thesis.
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Comparison of success rate of multi-channel methods of speech signal separation
Přikryl, Petr ; Zezula, Radek (referee) ; Míča, Ivan (advisor)
The separation of independent sources from mixed observed data is a fundamental problem in many practical situations. A typical example is speech recordings made in an acoustic environment in the presence of background noise or other speakers. Problems of signal separation are explored by a group of methods called Blind Source Separation. Blind Source Separation (BSS) consists on estimating a set of N unknown sources from P observations resulting from the mixture of these sources and unknown background. Some existing solutions for instantaneous mixtures are reviewed and in Matlab implemented , i.e Independent Componnent Analysis (ICA) and Time-Frequency Analysis (TF). The acoustic signals recorded in real environment are not instantaneous, but convolutive mixtures. In this case, an ICA algorithm for separation of convolutive mixtures in frequency domain is introduced and in Matlab implemented. This diploma thesis examines the useability and comparisn of proposed separation algorithms.
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Human Sleep EEG Analysis
Sadovský, Petr ; Rozman, Jiří (advisor)
This thesis deals with analysis and processing of the Sleep Electroencephalogram (EEG) signals. The scope of this thesis can be split into several areas. The first area is application of the Independent Component Analysis (ICA) method for EEG signal analysis. A model of EEG signal formation is proposed and conditions under which this model is valid are examined. It is shown that ICA can be used to remove non-deterministic artifacts contained in the EEG signals. The second area of interest is analysis of stationarity of the Sleep EEG signal. Methods to identify stationary signal segments and to analyze statistical properties of these stationary segments are presented. The third area of interest focuses on spectral analysis of the Sleep EEG signals. Analyses are performed that shows the processes that form particular parts of EEG signals spectrum. Also, random signals that are an integral part of the EEG signals analysis are performed. The last area of interest focuses on elimination of the transition processes that are caused by the filtering of the short EEG signal segments.
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Implemetation of algorithms for blind source separation in C/C++ language
Funderák, Marcel ; Malý, Jan (referee) ; Míča, Ivan (advisor)
This thesis is describing one of the methods of Blind Source Separation (BSS) which is Independent Component Analysis. There is shown some brief introduction to the theory behind in which there are explained some basic findings. These findings are important for understanding the theory behind algorithms of ICA. These theoretical findings include primarily explanations of basic knowledge of statistics science. In next part there are described methods which are advisable for preprocessing of input signals – mainly Principal Component Analysis (PCA) and whitening of signals. Mainly whitening is very important part of solution of ICA algorithms. Then there are described different ICA algorithm solutions and especially introduction in this problematic. FastICA algorithm description is mainly depicted because it is very good for computer processing since it is strong and it is less computer demanding than other algorithms. After that follows implementation of one of the ICA algorithm in C++ programming language. FastICA algorithm for complex valued signal was chosen.
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Dectection of brain wakefulness from scalp EEG data with higher order statistics
Semeráková, Nikola ; Ronzhina, Marina (referee) ; Labounek, René (advisor)
Presented master's thesis deals with detection of brain wakefulness from scalp EEG data with higher order statistics. Part of the thesis is a description of electroencephalography, from the method of signal generation, sensing, electroencephraphy, EEG signal artifacts, frequency bands of EEG signal to its possible processing. Furthermore, the concept of mental fatigue and the possibility of its detection in the EEG signal is described. Subsequently, the principles of higher statistical methods of PCA and ICA and the specific possibilities of decomposition of EEG signal are described using these methods, from which the method of group spatial-frequency ICA was chosen as a suitable method for selection of partial oscillatory sources in EEG signal. In the next part there is described a method of acquisition of data, a the suggestion of solution with selected method and a description of the implemented algorithm, that was applied to real 256-lead scalp EEG data captured during a block task focused on subject allertnes. The absolute and relative power of the EEG signal was decomposed. From the achieved results, we observe that the fluctuations of the spatial frequency patterns of relative power (especially for theta and alpha bands) significantly more closely correspond with the change of reaction time and the error of the subjects performing the task. These observations appear to be relatively consistent with previously published literature, and the current study shows that spatial frequency ICA is able to blindly isolate space-frequency patterns whose fluctuations are statistically significantly correlated with parameters (reaction time, error rate) directly flowing from the given task.
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Evaluation of eye-blinking artifact effect on fusion result of simultaneous EEG-fMRI data
Dobiš, Lukáš ; Jakubíček, Roman (referee) ; Labounek, René (advisor)
This thesis sets a theoretical framework about simultaneous EEG-fMRI fusion. The work contains a description of basic principles of acquisition, their individual artifact types and preprocessing techniques for each type of data. Thesis mainly deals with suppression of eye blink artifacts in EEG data, by the method of independent component analysis. The following part explains the technique of simultaneous EEG-fMRI fusion in a general linear model and the creation of activation maps of statistically important correlations. This chapter is concluded with a description of methodology needed for result analysis. Finally, the used data are described, and a solution is proposed and applied in process of EEG preprocessing with artifact suppression, data fusion and result evaluation in MATLAB environment. Evaulation results showed that eye blink artifact influences the fusion result computed from relative power values more then that constructed via absolute power values. Tested method didnt supress eye blink artifact completely.
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Non-contact detection of physiological parameters from image sequences
Bršlicová, Tereza ; Janoušek, Oto (referee) ; Kolář, Radim (advisor)
This thesis deals with the study of contactless and non-invasive methods for estimating heart and respiratory rate. Non-contact measurement involves sensing persons by using camera and the values of the physiological parameters are then assessed from the sets of image sequences by using suitable approaches. The theoretical part is devoted to description of the various methods and their implementation. The practical part describes the design and realization of the experiment for contactless detection of heart and respiratory rate. The experiment was carried out on 10 volunteers with a known heart and respiratory rate, which was covered by using of a sophisticated system BIOPAC. Processing and analysis of the measured data was conducted in software environment Matlab. Finally, results from contactless detection were compared with the reference from measurement system BIOPAC. Experiment results are statistically evaluated and discussed.
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