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dc.contributor.authorLee, Daniel Yu Shengen_US
dc.identifier.citationLee, D. Y. S. (2021). Music generation with deep learning techniques. Final Year Project (FYP), Nanyang Technological University, Singapore.
dc.description.abstractThis report demonstrated the use of conditioning inputs, together with an appropriate model architecture, to improve the structure of generated music through deep learning. Existing challenges to generate music using deep learning, in particular structure, were reviewed. The use of bar counter, occurrence of repeated motifs, and form of a piece as conditioning inputs were hypothesized to capture long-term structure of music. Then, the proposed model was designed using Bidirectional Long Short-Term Memory (Bi-LSTM) and attention layers to take in the conditioning inputs. To evaluate performance of the proposed model, quantitative analysis was done on the proposed model, the same model without conditioning inputs, and a baseline LSTM model. Following which, a user study was conducted to compare music samples generated by the 3 models. Evaluation results verified that by utilising the 3 conditioning inputs, the proposed model generated more pleasant-sounding and structurally coherent music.en_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Computer science and engineering::Computing methodologies::Artificial intelligenceen_US
dc.titleMusic generation with deep learning techniquesen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorAlexei Sourinen_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Science)en_US
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Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
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