Show simple item record

dc.contributor.advisorBremdal, Bernt
dc.contributor.advisorDadman, Shayan
dc.contributor.authorEldby, Tor
dc.description.abstractIn this thesis, the ability of CharRNN models learning to compose guitar music using varying representations of guitar tablature is explored. I utilize a well-versed sequential model of LSTM cells, and investigate the ability of said model to input, and predict both character to character, and sequence to sequence, following the principles of natural language processing and music information retrieval. The study was conducted on datasets consisting of data naïvely retrieved from a subset of classical guitar tablature. With regards to tablature structure, the experiments uncover a clearly superior form for character to character prediction, producing a model capable of composing seemingly musically coherent phrases. The work is not fully able to compare the character predictor with the sequence predictor and further details how this could potentially be alleviated.en_US
dc.publisherUiT Norges arktiske universitetno
dc.publisherUiT The Arctic University of Norwayen
dc.rights.holderCopyright 2021 The Author(s)
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)en_US
dc.subjectVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550::Datateknologi: 551en_US
dc.subjectVDP::Technology: 500::Information and communication technology: 550::Computer technology: 551en_US
dc.titleInvestigating representation of tablature data for NLP music predictionen_US
dc.typeMaster thesisen

File(s) in this item


This item appears in the following collection(s)

Show simple item record

Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)