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Image annotation using deep learning
Zarapina, Natalya ; Rajnoha, Martin (referee) ; Burget, Radim (advisor)
This semester thesis describes the design and implementation of the client-server program for classification and localization of certain elements which are present in provided images. This program loads a set of images and use deep learning, especially deep convolution neural network perform a classification. First part describes the architecture, basic principles of operations in convolution network and chosen machine learning algorithms for classification. Second part contains a description of created program.
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Deep Learning for Text Classification
Kolařík, Martin ; Harár, Pavol (referee) ; Povoda, Lukáš (advisor)
Thesis focuses on analysis of contemporary machine learning methods used for text classification based on emotion and testing several deep neural nework architectures. Outcome of this thesis is a neural network architecture, which is tuned for using with text data and which had the best result of 79,94 percent. Proposed method is language independent and it doesn’t require as precisely classified training datasets as current methods. Training and testing datasets were consisted of short amateur movie reviews in Czech and in English. Thesis contains also overview of theoretical basics for convolutional neural networks and history of neural networks and language processing Scripts were written in Python, neural networks were simulated using Keras library and Theano framework. We used CUDA for better performance.
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Music, Speech, Crying, Singing Detection in Audio (Video)
Danko, Michal ; Malenovský, Vladimír (referee) ; Szőke, Igor (advisor)
This thesis follows the trend of last decades in using neural networks in order to detect speech in noisy data. The text begins with basic knowledge about discussed topics, such as audio features, machine learning and neural networks. The network parameters are examined in order to provide the most suitable background for the experiments. The main focus of the experiments is to observe the influence of various sound events on the speech detection on a small, diverse database. Where the sound events correlated to the speech proved to be the most beneficial. In addition, the accuracy of the acoustic events, previously used only as a supplement to the speech, is also a part of experimentation. The experiment of examining the extending of the datasets by more fairly distributed data shows that it doesn't guarantee an improvement. And finally, the last experiment demonstrates that the network indeed succeeded in learning how to predict voice activity in both clean and noisy data.
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Image annotation using deep learning
Zarapina, Natalya ; Rajnoha, Martin (referee) ; Burget, Radim (advisor)
This semester thesis describes the design and implementation of the client-server program for classification and localization of certain elements which are present in provided images. This program loads a set of images and use deep learning, especially deep convolution neural network perform a classification. First part describes the architecture, basic principles of operations in convolution network and chosen machine learning algorithms for classification. Second part contains a description of created program.
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Deep Learning for Text Classification
Kolařík, Martin ; Harár, Pavol (referee) ; Povoda, Lukáš (advisor)
Thesis focuses on analysis of contemporary machine learning methods used for text classification based on emotion and testing several deep neural nework architectures. Outcome of this thesis is a neural network architecture, which is tuned for using with text data and which had the best result of 79,94 percent. Proposed method is language independent and it doesn’t require as precisely classified training datasets as current methods. Training and testing datasets were consisted of short amateur movie reviews in Czech and in English. Thesis contains also overview of theoretical basics for convolutional neural networks and history of neural networks and language processing Scripts were written in Python, neural networks were simulated using Keras library and Theano framework. We used CUDA for better performance.
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Music, Speech, Crying, Singing Detection in Audio (Video)
Danko, Michal ; Malenovský, Vladimír (referee) ; Szőke, Igor (advisor)
This thesis follows the trend of last decades in using neural networks in order to detect speech in noisy data. The text begins with basic knowledge about discussed topics, such as audio features, machine learning and neural networks. The network parameters are examined in order to provide the most suitable background for the experiments. The main focus of the experiments is to observe the influence of various sound events on the speech detection on a small, diverse database. Where the sound events correlated to the speech proved to be the most beneficial. In addition, the accuracy of the acoustic events, previously used only as a supplement to the speech, is also a part of experimentation. The experiment of examining the extending of the datasets by more fairly distributed data shows that it doesn't guarantee an improvement. And finally, the last experiment demonstrates that the network indeed succeeded in learning how to predict voice activity in both clean and noisy data.
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