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Tempo detector based on a neural network
Suchánek, Tomáš ; Smékal, Zdeněk (referee) ; Ištvánek, Matěj (advisor)
This Master’s thesis deals with beat tracking systems, whose functionality is based on neural networks. It describes the structure of these systems and how the signal is processed in their individual blocks. Emphasis is then placed on recurrent and temporal convolutional networks, which by they nature can effectively detect tempo and beats in audio recordings. The selected methods, network architectures and their modifications are then implemented within a comprehensive detection system, which is further tested and evaluated through a cross-validation process on a genre-diverse data-set. The results show that the system, with proposed temporal convolutional network architecture, produces comparable results with foreign publications. For example, within the SMC dataset, it proved to be the most successful, on the contrary, in the case of other datasets it was slightly below the accuracy of state-of-the-art systems. In addition,the proposed network retains low computational complexity despite increased number of internal parameters.
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Active Learning for OCR
Kohút, Jan ; Kolář, Martin (referee) ; Hradiš, Michal (advisor)
The aim of this Master's thesis is to design methods of active learning and to experiment with datasets of historical documents. A large and diverse dataset IMPACT of more than one million lines is used for experiments. I am using neural networks to check the readability of lines and correctness of their annotations. Firstly, I compare architectures of convolutional and recurrent neural networks with bidirectional LSTM layer. Next, I study different ways of learning neural networks using methods of active learning. Mainly I use active learning to adapt neural networks to documents that the neural networks do not have in the original training dataset. Active learning is thus used for picking appropriate adaptation data. Convolutional neural networks achieve 98.6\% accuracy, recurrent neural networks achieve 99.5\% accuracy. Active learning decreases error by 26\% compared to random pick of adaptations data.
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Deep Neural Networks for Text Recognition
Kavuliak, Daniel ; Hradiš, Michal (referee) ; Kišš, Martin (advisor)
The aim of this work is to build a model for handwritten text recognition, which will use non-autoregressive decoder. This type of decoder calculates character predictions independently of other predicted characters, which can be advantageous in terms of inference speed, but the quality of the prediction is worse. The motivation is to design a non-autoregressive decoder, which will have the task of refining the encoder's predictions. The task was solved with the help of decoders, which mask the encoder's predictions or partially suppress the information due to the use of information about unmasked symbols or using input sequence information. Subsequently, a series of experiments was performed, where the best model reached a character error rate of 8.92 %. But the assignment was not fulfilled, because the encoder itself reached 6.38 %.
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Classification with Use of Neural Networks in the Keras Environment
Pyšík, Michal ; Burgetová, Ivana (referee) ; Bartík, Vladimír (advisor)
Tato práce zkoumá problematiku klasifikace pomocí umělých neuronových sítí s využitím knihovny Keras, poskytující vysokoúrovňové rozhraní pro práci s umělými neuronovými sítěmi v programovacím jazyce Python. Cílem práce je prozkoumat rozsáhlé možnosti této knihovny v oblasti klasifikace a porovnat různé typy a topologie umělých neuronových sítí formou experimentů na vybraných datasetech, což je doplněno jednoduchou experimentální aplikací sloužící především jako rozhraní pro tyto experimenty.
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Reservoir Computing for Industrial Applications
Brhel, Jakub ; Bražina, Jakub (referee) ; Kovář, Jiří (advisor)
In this bachelor thesis, the field of reservoir computing and its application in the industrial sector is investigated. The main objective of the thesis is to verify the effectiveness and accuracy of the echo state network method in time series prediction. To achieve this objective, a dataset from a manufacturing machine is used and the echo state network algorithm is implemented. The results are analysed and are also compared with the results of previous studies in the field of reservoir computing.
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EEG Classification Model for Emotion Detection Using Python
Vengerová, Veronika ; Zaheer, Muhammad Asad (referee) ; Jawed, Soyiba (advisor)
Táto práca sa zaoberá rozoznávaním emócií z elektroencefalogramu (EEG). Dva modely na binárnu klasifikáciu emócií, kde jeden model klasifikuje neutrálnu emóciu alebo strach a druhý šťastie a smútok. Počas práce boli vyskúšané mnohé rôzne architektúry, pričom najlepšie výsledky boli dosiahnuté modelom pozostávajúcim z dvoch vetiev KNN-LSTM spojenými pred výstupnou vrstvou. Výsledná presnosť bola 87.309% na klasifikáciu šťastia a smútku a 84.865% na klasifikáciu neutrálnej emócie a strachu.
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Time-Series Analysis and Prediction by Means of Neural Networks
Kňažovič, Martin ; Jaroš, Jiří (referee) ; Bidlo, Michal (advisor)
This thesis deals with stock price prediction based on the creation of prediction models for selected stocks (BRK-A, GOOG, and MSFT), which can help investors in the creation of their financial decisions or by replacing other stock prediction models in existing prediction systems. Models created in this thesis are presented in two types - univariate model and multivariate model, which are in their final version presented in two architectures, one-layer architecture and two-layer architecture. Discussed models are created by means of neural networks, specifically recurrent neural networks with its extension - Long short-term memory. The output of the presented models is a forecast of the next-day stock price, which can be used for evaluating the right time to buy or sell a given stock. The quality of individual prediction models is evaluated via the mean squared error of the validation or testing dataset or alternatively based on stock price trend prediction.
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Improving Consistency in Text Recognition Datasets
Tvarožný, Matúš ; Hradiš, Michal (referee) ; Kišš, Martin (advisor)
This work is concerned with increasing the consistency of datasets for text recognition. This paper describes the problems that cause the inconsistency and then presents solutions to eliminate it. The effect of the properties of the polygons defining the text line boundaries and hence how the modified version of the dataset, which is composed of ideal text line variants, affected the accuracy of the model is investigated. Further, the work focuses on detecting and then removing or modifying text lines whose ground truth transcription does not match the actual text they contain. Experimentation showed that removing the visual inconsistency on the training set did not have a significant effect on the trained model, but modifying the test set improved the OCR accuracy of the model by 1.1\% CER. By modifying the dataset so that it did not contain mutually inconsistent pairs of recognized text and the corresponding ground truth, the model improved by a maximum of only 0.2\% CER after re-training. The main finding of this work is, above all, the proven beneficial effect of removing inconsistencies on test suites, thanks to which it is possible to determine a more realistic error rate of the OCR model.
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Web application for Cybersecurity Job Ads Analysis
Turek, Adam ; Sikora, Marek (referee) ; Ricci, Sara (advisor)
Cílem bakalářské práce je vytvoření interaktivní celosvětové mapy zobrazující databázi pracovních inzerátů ve webové aplikaci a provedení filtrování podle různých parametrů, kde je následně provedena analýza strojového učení. Také mapa zobrazuje počet inzerátů na pracovní pozice podle příslušných států. Webová aplikace je vytvořena pomoci JavaScriptové knihovny ReactJS spojené s LeafletJS, které zajišťují hlavní funkcionalitu. Část se strojovým učením a změna skriptů je realizována pomocí programovacího jazyku Python. Práce popisuje teoretickou část a implementaci jednotlivých funkcí mapy a dále se zabývá popisem a úspěsnou úpravou skriptů pro účely provedení strojového učení.
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