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.
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:220599 |
| Date | January 2014 |
| Creators | Ozdobinski, Roman |
| Contributors | Rajmic, Pavel, Mach, Václav |
| Publisher | Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií |
| Source Sets | Czech ETDs |
| Language | Czech |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
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