Music Generation Using Deep Learning Techniques

dc.contributor.advisor Evren Güney
dc.contributor.author Akalın, Kutay
dc.date.accessioned 2021-12-14T11:21:13Z
dc.date.available 2021-12-14T11:21:13Z
dc.date.issued 2021
dc.description.abstract This project aims to generate songs using the Jukebox model and its architecture. Jukebox’s Vector Quantized Variational AutoEncoder (VQ-VAE) architecture is state-of-the-art deep generative model used for music generation and gives an outstanding result. For this purpose, different Elvis Presley songs were analyzed in audio domain using various Music Information Retrieval (MIR) methods. The top level of the Jukebox model was retrained with these songs in order to increase the quality of the songs that will be produced in the style of Elvis Presley. After that, 3 new samples were generated using the first six seconds of Elvis Presley - Jailhouse Rock as the input signal. At the end, these new songs were analyzed and compared.
dc.identifier.citation Akalın, K. (2021). Music Generation Using Deep Learning Techniques. MEF Üniversitesi Fen Bilimleri Enstitüsü, Büyük Veri Analitiği Yüksek Lisans Programı. ss. 1-24
dc.identifier.uri https://hdl.handle.net/20.500.11779/1699
dc.language.iso en
dc.publisher MEF Üniversitesi Fen Bilimleri Enstitüsü
dc.rights info:eu-repo/semantics/openAccess
dc.subject Derin Öğrenme, Müzik Üretimi, VQ-VEA, Ses Sinyali İşleme
dc.title Music Generation Using Deep Learning Techniques
dc.title.alternative Derin öğrenme teknikleri ile müzik üretimi
dc.type Masters Term Project
dspace.entity.type Publication
gdc.author.institutional Akalın, Kutay
gdc.coar.access open access
gdc.coar.type other
gdc.description.department Lisansüstü Eğitim Enstitüsü, Büyük Veri Analitiği Yüksek Lisans Programı
gdc.description.endpage 24
gdc.description.publicationcategory YL-Bitirme Projesi
gdc.description.startpage 1
gdc.publishedmonth N/A
relation.isAuthorOfPublication.latestForDiscovery 6cd6fa8d-207e-4ab4-a977-c3a42684f2d1
relation.isOrgUnitOfPublication.latestForDiscovery 636850bf-e58c-4b59-bcf0-fa7418bb7977

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