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Search for a dictionary for audiosignals
Martinek, Václav ; Záviška, Pavel (referee) ; Rajmic, Pavel (advisor)
This thesis should answer what constitutes audiosignal. It deals with the dictionary learning based on sparse representations of signals. This thesis describing algorithms, which are important for the creation of learning dictionaries. We find a comparison of signal representation using Fourier transform and learned vocabulary. It describes the creation of a database of musical sounds.
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Dictionary learning for sparse signal reconstruction
Ozdobinski, Roman ; Rajmic, Pavel (referee) ; Mach, Václav (advisor)
This bachelor thesis discusses the dictionary learning for the reconstruction of signal based on sparse representations. There are methods of static and optimized dictionary of matrices described that are used with approximative Orthogonal Matching Pursuit algorithm to reconstruct the missing groups of samples in the audio signal. There is theoretically analyzed algorithm for learning K-SVD dictionary together with its implementation in Matlab. Furthermore, the selected dictionaries are compared to the various types of audio signals.
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Applications of Dictionary Learning Methods for Audio Inpainting
Ozdobinski, Roman ; Rajmic, Pavel (referee) ; Mach, Václav (advisor)
This diploma thesis discusses methods of dictionary learning to inpaint missing sections in the audio signal. There was theoretically analyzed and practically used algorithms K-SVD and INK-SVD for dictionary learning. These dictionaries have been applied to the reconstruction of audio signals using OMP (Orthogonal Matching Pursuit). Furthermore, there was proposed an algorithm for selecting the stationary segments and their subsequent use as training data for K-SVD and INK-SVD. In the practical part of thesis have been observed efficiency with training set selection from whole signal compared with algorithm for stationary segmentation used. The influence of mutual coherence on the quality of reconstruction with incoherent dictionary was also studied. With created scripts for multiple testing in Matlab, there was performed comparison of these methods on genre distinct songs.
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Optimization of data representation for target tracking using sensor network
Cabalová, Klára ; Veselý, Vítězslav (referee) ; Rajmic, Pavel (advisor)
The aim of this bachelor thesis is to find optimal data representation for target tracking using sensor network. There is described a model of decentralized sensor network and also the application of so called dictionary to represent the measured data. Also, there is theoretically introduced the K-SVD algorithm that is used for dictionary learning and there are learnt dictionaries for data representation based on the model signals. These dictionaries are compared with each other.
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Search for a dictionary for audiosignals
Martinek, Václav ; Záviška, Pavel (referee) ; Rajmic, Pavel (advisor)
This thesis should answer what constitutes audio signal. It deals with the dictionary learning based on sparse representations of signals. This thesis describing algorithms, which are important for the creation of learning dictionaries. We find a comparison of signal representation using Fourier transform and learned vocabulary. It describes the creation of a database of musical sounds.
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Determining the optimal patch size for sparse image representation
Šuránek, David ; Zátyik, Ján (referee) ; Špiřík, Jan (advisor)
Introduction of this thesis is dedicated to the description of basic concepts and algorithms for image processing using sparse representation. Furthermore there is mentioned neural network model called Restricted Boltzmann machine, which is in the practical part of the thesis subject of behaving observation in the task of determining the optimal block size for extrapolation using K-SVD algorithm
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Search for a dictionary for audiosignals
Martinek, Václav ; Záviška, Pavel (referee) ; Rajmic, Pavel (advisor)
This thesis should answer what constitutes audiosignal. It deals with the dictionary learning based on sparse representations of signals. This thesis describing algorithms, which are important for the creation of learning dictionaries. We find a comparison of signal representation using Fourier transform and learned vocabulary. It describes the creation of a database of musical sounds.
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Search for a dictionary for audiosignals
Martinek, Václav ; Záviška, Pavel (referee) ; Rajmic, Pavel (advisor)
This thesis should answer what constitutes audio signal. It deals with the dictionary learning based on sparse representations of signals. This thesis describing algorithms, which are important for the creation of learning dictionaries. We find a comparison of signal representation using Fourier transform and learned vocabulary. It describes the creation of a database of musical sounds.
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Optimization of data representation for target tracking using sensor network
Cabalová, Klára ; Veselý, Vítězslav (referee) ; Rajmic, Pavel (advisor)
The aim of this bachelor thesis is to find optimal data representation for target tracking using sensor network. There is described a model of decentralized sensor network and also the application of so called dictionary to represent the measured data. Also, there is theoretically introduced the K-SVD algorithm that is used for dictionary learning and there are learnt dictionaries for data representation based on the model signals. These dictionaries are compared with each other.
|
|
|
Dictionary learning for sparse signal reconstruction
Ozdobinski, Roman ; Rajmic, Pavel (referee) ; Mach, Václav (advisor)
This bachelor thesis discusses the dictionary learning for the reconstruction of signal based on sparse representations. There are methods of static and optimized dictionary of matrices described that are used with approximative Orthogonal Matching Pursuit algorithm to reconstruct the missing groups of samples in the audio signal. There is theoretically analyzed algorithm for learning K-SVD dictionary together with its implementation in Matlab. Furthermore, the selected dictionaries are compared to the various types of audio signals.
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