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Reverzní inženýring mixáže pomocí neuronové sítě
Čermák, Jerguš ; Mokrý, Ondřej (oponent) ; Rajmic, Pavel (vedoucí práce)
Bakalárska práca sa zaoberá využitím algoritmov neurónovej siete za účelom zistenia parametrov signálových procesorov použitých pri mixáži zvukových stôp. V rámci práce sú prezentované lineárne signálové procesory \emph{Gain}, \emph{Pan}, \emph{Filter} a \emph{Reverb} umožňujúce úpravu zvukového signálu a vytvorenie stereofónneho mixu zvukovej nahrávky. Následne sú vďaka implementácií pomocou knižnice DDSP použité v zmysle vrstiev modelu neurónovej siete, ktorý je zameraný na predikciu parametrov použitých pri mixáži, za predpokladu znalosti vstupných stôp a cieľového mixu. V rámci práce boli vytvorené stereofónne mixy, ktorých parametre boli následne odhadované pomocou dvoch modelov neurónovej siete. Výsledky boli posudzované ako objektívnymi, tak subjektívnymi metódami (posluchovým testom).
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Use of Diffusion Models in Deepfakes
Trúchly, Dominik ; Malinka, Kamil (oponent) ; Lapšanský, Tomáš (vedoucí práce)
A deepfake is a type of synthetic media created through sophisticated machine learning algorithms, particularly deep neural networks. As an example Generative adversarial neural networks (GANs), that are capable of generating images that are almost impossible for ordinary individuals to differentiate from genuine reality. Consequently, deepfake detection algorithms have been developed to address this growing concern. Leveraging advanced machine learning techniques, these algorithms analyze various features within images and videos to identify inconsistencies or anomalies indicative of manipulation. This thesis investigates the application of diffusion models, commonly utilized in digital image processing to enhance image quality by reducing noise and blurring, in bolstering the realism of deepfakes. By using these models, we test their effect on detecting deepfakes images using deepfake detectors.
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Implementing gesture recognition on ARM as an alternative to traditional device control
Gajdošík, Richard ; Zbořil, František (oponent) ; Kočí, Radek (vedoucí práce)
This bachelor's thesis focuses on the development and implementation of a gesture recognition system on ARM architecture, utilizing the i.MX 93 board and TensorFlow Lite. The project is grounded in the application of neural networks for the recognition of hand gestures, offering an alternative to traditional device control methods. An integral part of the work involves a comprehensive analysis of existing gesture recognition solutions, identifying their strengths and potential improvements. The thesis elaborates on the design, development, and optimization of a real-time gesture recognition model specifically for ARM chips, emphasizing efficiency and performance. Additionally, the thesis covers the creation of a demonstrative application that visually represents recognized gestures. User testing is conducted to evaluate the practicality and user experience of the gesture recognition system, providing valuable feedback for future enhancements.
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Neural Networks for Video Quality Enhancement
Sirovatka, Matej ; Juránek, Roman (oponent) ; Hradiš, Michal (vedoucí práce)
In this thesis, a new method for video super-resolution is proposed. The method is based on the idea of using deformable convolutional layers together with optical flow to align features from multiple sequential video frames. This novel module is then used in a U-Net-like deep neural network to predict high-resolution frames. The proposed method is evaluated on a dataset containing real-life scenes and compared to other methods. Multiple different configurations of the proposed method are tested and the results are analyzed. The results of the experiments show promising results, with the model outperforming bilinear interpolation, and single-frame methods. Multiple different architectures of the feature alignment module together with the rest of the U-Net architecture are tested, showing that using Vgg19 as the encoder of the U-Net gives the best results.
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Very Low Bit-Rate Speech Coding Based on Neural Networks
Jochman, Stanislav ; Malenovský, Vladimír (oponent) ; Černocký, Jan (vedoucí práce)
During this work, we focused on replicating and enhancing results by using the neural network LPCNet. We compared audio quality from the pre-trained model and our models trained on smaller datasets, thus reducing training time and improving audio quality. We determined that using a language-specific dataset can produce greater results in that specific language than a big general model. We measured the quality of speech of the pre-trained model and our models using WARPQ ranking score 5.2.4. We also examined possibilities of improving audio quality by filtering output audio using output post-filters and formant-enhancing filters. Our results show measurable improvement in audio quality using the suggested methods.
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Přehled současných přístupů ke klasifikacím
Brezánský, Tomáš ; Rozman, Jaroslav (oponent) ; Zbořil, František (vedoucí práce)
Táto bakalárska práca sa zaoberá prehľadom súčasných prístupov ku klasifikáciám. Popisuje rôzne prístupy ku klasifikáciám a ich algoritmy, zameriava sa na neuronové siete, bayesové klasifikátory a rozhodovacie stromy. Hlavnou úlohou tejto prace je vykonať experimenty s tromi klasifikačnými algoritmami, konkrétne sú to, algoritmus ID3, RCE neurónová sieť a naivný bayesov klasifikátor. Práca obsahuje experimenty s danými algoritmami a vyhodnocuje získané výsledky.
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Named Entity Recognition Exploiting Sub Word Information
Dobrovodský, Patrik ; Egorova, Ekaterina (oponent) ; Kesiraju, Santosh (vedoucí práce)
The aim of this thesis is the creation of a Named Entity Recognition system based on an older state-of-the-art model and studying how subword information can improve the recognition of out-of-vocabulary words. This proposed system besides English has to support two additional Indo-European languages: German and Hungarian. This work features a named entity tagger based on deep learning using pretrained and custom-trained word embeddings, sparse features, and character embeddings extracted by a Convolutional Neural Network. All these features are then processed by sequence-based (bidirectional Long Short-Term Memory) and feature-based (Conditional Random Field) approaches with the goal of achieving a F1-score similar to the work it is based on, and to compare how far present time state-of-the-art systems have evolved. The result is a system that achieves a 90.98% F1-score on the CoNLL 2003 English test dataset using pretrained word embeddings, not far behind the original work's 91.26%. For the other two languages, the model scores 89.34% on the WikiAnn German test dataset and 93.04% on the WikiAnn Hungarian test dataset with the usage of custom-trained embeddings.
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Software pro detekci a rozpoznání registrační značky vozidla
Masaryk, Adam ; Hradiš, Michal (oponent) ; Špaňhel, Jakub (vedoucí práce)
Cieľom tejto bakalárskej práce je navrhnúť a vyvinúť softvér, ktorý dokáže detegovať a rozpoznávať registračné značky z obrázkov. Softvér je rozdelený na 3 časti - detekcia značky, spracovanie výstupu detektora a rozpoznanie znakov na registračnej značke. Detekciu a rozpoznanie sme sa rozhodli implementovať pomocou moderných metód využitím konvolučných neurónových sietí.
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Horizon Detection in Image
Holková, Natália ; Herout, Adam (oponent) ; Juránek, Roman (vedoucí práce)
This thesis aims to implement a method of detecting the horizon line in images using deep learning to prevent any constraints on input data. A training dataset is created by downloaded images from large metropolitan cities around the world using the Google Street View service. Several popular architectures for convolutional neural networks are chosen, and their performance is evaluated on existing benchmark datasets.
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Deep Neural Networks Used for Customer Support Cases Analysis
Marušic, Marek ; Ryšavý, Ondřej (oponent) ; Pluskal, Jan (vedoucí práce)
Artificial intelligence is remarkably popular these days. It can be used to resolve various highly complex tasks in fields such as image processing, sound processing, natural language processing, etc. Red Hat has an extensive database of resolved support cases. Therefore an idea was proposed to use these data for data mining and information retrieval in order to ease a resolution process of the support cases. In this work, various deep neural network models were created for prediction of features which could help during the resolution process. Techniques and models used in this work are described as well as their performance in the specific tasks. Comparison of individual models is outlined as well.
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