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        Removing noise from audio recording using Online Non-negative Matrix Factorization

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        Final Thesis Mischa Korthagen, 6435157 (Removing noise from audio recording using Online Non-negative Matrix Factorization).pdf (7.187Mb)
        Publication date
        2024
        Author
        Korthagen, Mischa
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        Summary
        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.
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        https://studenttheses.uu.nl/handle/20.500.12932/46761
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