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Hydrogen Production from Biomass II
Matějka, Pavel ; Lisý, Martin (referee) ; Houdková, Lucie (advisor)
This master thesis continues the master thesis Výroba vodíku z biomasy. In that work were performed first experiments of hydrogen production from beer less by dark fermentation. Further experiments will continue based on previous experiments. For the energy balance were used results from [1] and data obtained from literature searches, because the results of the experiments were not entirely satisfactory. Energy balance was calculated for a laboratory fermenter to evaluate the efficiency of energy obtainable from the hydrogen produced by fermentation in a dark laboratory conditions. It was compared the yield given in [1] and yield derived from a literature review. The conclusion presents recommendations for further research.
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Analysis of Interview Audio
Polok, Alexander ; Plchot, Oldřich (referee) ; Matějka, Pavel (advisor)
The aim of this thesis is the analysis of psychotherapeutic sessions. Classifiers describing the therapy are extracted from the audio recordings. These are then aggregated, compared with other sessions, and graphically presented in a report summarizing the conversation. In this way, therapists are provided with feedback that can serve for professional growth and better psychotherapy in the future.
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Multiplatform Application for Speaker Verification
Görig, Jan ; Matějka, Pavel (referee) ; Glembek, Ondřej (advisor)
Bachelor thesis considers speaker recognition without knowledge of spoken message. There are described current feature extraction methods and their evaluation using Gaussian mixture model. The practical output of this work is application for visualization of the recognition process. Developed application is cross platform and it uses Qt and BSAPI libraries.
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Codec Detection from Speech
Jon, Josef ; Matějka, Pavel (referee) ; Černocký, Jan (advisor)
Tato práce se zabývá detekcí kodeků z komprimovaného řečového signálu. Cílem bylo zjistit, jaké charakteristiky rozlišují jednotlivé kodeky a následně vytvořit prostředí vhodné pro experimenty s různými typy a konfiguracemi klasifikátorů. Použity byly Support vector machines a především neuronové sítě, které byly vytvořeny pomocí nástroje Keras. Hlavním přínosem této práce je experimentální část, ve které je analyzován vliv různých parametrů neuronové sítě. Po nalezení nejvhodnější kombinace parametrů dosáhla síť přesnosti klasifikace přes 98% na testovací sadě obsahující data z 6 kodeků.
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Modelling Prosodic Dynamics for Speaker Recognition
Jančík, Zdeněk ; Fapšo, Michal (referee) ; Matějka, Pavel (advisor)
Most current automatic speaker recognition system extract speaker-depend features by looking at short-term spectral information. This approach ignores long-term information. I explored approach that use the fundamental frequency and energy trajectories for each speaker. This approach models prosody dynamics on single fonemes or syllables. It is known from literature that prosodic systems do not work as well the acoustic one but it improve the system when fusing. I verified this assumption by fusing my results with state of the art acoustic system from BUT. Data from standard evaluation campaigns organized by National Institute of Standarts and Technology are used for all experiments.
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Robust Speech Activity Detection
Popková, Anna ; Plchot, Oldřich (referee) ; Matějka, Pavel (advisor)
The aim of this work is to design and create a robust speech activity detector that is able to detect speech in different languages, in a noise environment and with music on background. I decided to solve this problem by using a neural network as a classification model that assigns one of the four possible classes - silence, speech, music, or noise to the input of audio recording. The resulting tool is able to detect the speech in at least 12 languages. Speech with musical background up to 88 % accuracy and system success on noisy data reaches from 84 % (5 dB SNR) to 88 % (20 dB SNR). This tool can be used for speech activity detection in various research areas of speech processing. The main contribution is the elimination of music, which when not eliminated, significantly increases the error rate of systems for speaker identification or speech recognition.
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Home Information System
Knyrevich, Michail ; Matějka, Pavel (referee) ; Szőke, Igor (advisor)
This bachelor's thesis involves the development of the Informational system for home, especially its Energy module. Some existing informational systems of this topic are analyzed there. Further, there is a formal concept of self Energy module , which is based on gained information. The part of this concept is prediction system of energetic expenses, which uses a weather monitoring. Realization and testing with usage of web technologies (especially the Zend Framework) are placed at the end of my thesis.
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Web as a Source for Automatic Creation of Morphological Dictionary
Bulka, Pavol ; Matějka, Pavel (referee) ; Smrž, Pavel (advisor)
Creation of natural language words is based on rules, which are generally complex. Often it is very difficult or even impossible to describe them precisely in a formal way. That is why we use a morphological dictionary to process natural language. In this paper we discuss the creation of morphological dictionary from Slovak's top level domain web. We talk about web crawling, data processing for morphological analysis and data structures too. This document makes basic principle and conception of morphological analysis clear. Final system, which is described in this thesis, produces morphological dictionary. This dictionary can be use in various application, for example spell checker, machine translation and so on.
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Acoustic Scene Classification from Speech
Dobrotka, Matúš ; Glembek, Ondřej (referee) ; Matějka, Pavel (advisor)
The topic of this thesis is an audio recording classification with 15 different acoustic scene classes that represent common scenes and places where people are situated on a regular basis. The thesis describes 2 approaches based on GMM and i-vectors and a fusion of the both approaches. The score of the best GMM system which was evaluated on the evaluation dataset of the DCASE Challenge is 60.4%. The best i-vector system's score is 68.4%. The fusion of the GMM system and the best i-vector system achieves score of 69.3%, which would lead to the 20th place in the all systems ranking of the DCASE 2017 Challenge (among 98 submitted systems from all over the world).
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