| dc.rights.license | CC-BY-NC-ND | |
| dc.contributor.advisor | Salavenich, Palina | |
| dc.contributor.author | Korthagen, Mischa | |
| dc.date.accessioned | 2024-07-18T13:01:40Z | |
| dc.date.available | 2024-07-18T13:01:40Z | |
| dc.date.issued | 2024 | |
| dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/46761 | |
| dc.description.abstract | Dictionary learning has been shown to be an effective tool for signal processing. In this thesis, we look
at a specific version of dictionary learning called Online Non-negative Matrix Factorization (ONMF) and
apply it in the context of denoising musical recordings. We begin with a theoretical overview, highlighting
the motivation for ONMF, such as its ability to train on a dataset while only ever requiring a small part
of it to be loaded into memory. We then experimentally show that the denoising performance of ONMF
depends strongly on the types of signals being processed and, to a lesser degree, on the correct choice of
dictionary size for each signal. | |
| dc.description.sponsorship | Utrecht University | |
| dc.language.iso | EN | |
| dc.subject | In this thesis, we look at a specific version of dictionary learning called Online Non-negative Matrix Factorization (ONMF) andapply it in the context of denoising musical recordings. We begin with a theoretical overview, highlighting, among other things, the motivation for ONMF. We then experimentally show that the denoising performance of ONMF depends strongly on the types of signals being processed and, to a lesser degree, on the correct choice of dictionary size for each signal. | |
| dc.title | Removing noise from audio recording using Online Non-negative Matrix Factorization | |
| dc.type.content | Bachelor Thesis | |
| dc.rights.accessrights | Open Access | |
| dc.subject.keywords | ONMF, audio denoising, denoising, NMF, dictionary learning | |
| dc.subject.courseuu | Wiskunde | |
| dc.thesis.id | 34186 | |
