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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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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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