Music Accompaniment Generation Using a Conditional Generative Adversarial Network
Name
Priidik Meelo Västrik
Abstract
Generation of good quality music accompaniment is very useful for composers and music producers. Generative artificial neural networks are booming and there is recently an increasing amount of music generation models published. The aim of this Bachelor’s thesis is to generate music accompaniment using spectrograms and an image translation model Pix2Pix. Experiments are conducted to generate different types of accompaniments. The best results are achieved when generating the drum stem. It can be seen from the results that generative adversarial networks’ outputs contain unnatural artifactsthat affect the results badly. Preventing this requires lots of finetuning.
Graduation Thesis language
Estonian
Graduation Thesis type
Bachelor - Computer Science
Supervisor(s)
Anna Aljanaki
Defence year
2024