Deep Learning for Continuous Symbolic Melody Generation Conditioned on Lyrics and Initial melodies

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Journal Title
Journal ISSN
Volume Title
Perustieteiden korkeakoulu | Master's thesis
Date
2023-08-21
Department
Major/Subject
Security and Cloud Computing
Mcode
SCI3113
Degree programme
Master’s Programme in Security and Cloud Computing (SECCLO)
Language
en
Pages
60+1
Series
Abstract
Symbolic music generation is an interdisciplinary research area, combining machine learning and music theory. This project focuses on the intersection of two problems within music generation, namely generating continuous music following a given seed (introduction), and rhythmically matching given lyrics. It enables artists to use AI as creative aid, obtaining a complete song having only written the lyrics and an initial melody. We propose a method for targeted training of a recursive Generative Adversarial Network (GAN) for initial melody conditioned generation, and explore the possibilities of using other state-of-the-art deep learning generation techniques, such as Denoising Diffusion Probabilistic Models (DDPMs), Long-short-term-memory networks (LSTMs) and the attention mechanism.
Description
Supervisor
Jung, Alexander
Thesis advisor
Yu, Yi
Keywords
music, machine earning, generation, deep earning, melodies, MIDI
Other note
Citation