University of Twente Student Theses
AI-Composed Music for User Preference Using Reinforcement Learning
Mysliwiec, D. (2023) AI-Composed Music for User Preference Using Reinforcement Learning.
|
PDF
594kB |
| Abstract: | Artificial intelligence has become a powerful tool in automatic music generation and music recommendation systems thanks to the field's rapid development. Nonetheless, few solutions have been proposed for easily generating music based on a non-expert user's preference. The results of an extensive literature review show that the main issue to combat was that of subjectivity in music, and gave an indication that a reinforcement learning algorithm combined with a deep learning model for user preference could be an appropriate solution. Based on these findings a new automatic music composition system that relies on a reinforcement learning algorithm and models preferences based on the user's ratings of transformer-generated pieces, improving tailoring to the user over iterations of the algorithm. The system was evaluated through human interaction and shows promising results with a 44.7\% improvement to the mean user rating of generated pieces in 6 iterations of the algorithm. The rating prediction model also achieves high accuracy with a difference between the predicted and received ratings of just 0.81 on a 10-point scale. Because the system relies on no prior knowledge, with such effectiveness it could give access to music compositions for user preference to a wider non-expert audience. |
| Item Type: | Essay (Bachelor) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science |
| Programme: | Computer Science BSc (56964) |
| Link to this item: | https://purl.utwente.nl/essays/96026 |
| Export this item as: | BibTeX EndNote HTML Citation Reference Manager |
Show download statistics for this publication
Show download statistics for this publicationRepository Staff Only: item control page