Summary of Musicrl: Aligning Music Generation to Human Preferences, by Geoffrey Cideron et al.
MusicRL: Aligning Music Generation to Human Preferences
by Geoffrey Cideron, Sertan Girgin, Mauro Verzetti, Damien Vincent, Matej Kastelic, Zalán Borsos, Brian McWilliams, Victor Ungureanu, Olivier Bachem, Olivier Pietquin, Matthieu Geist, Léonard Hussenot, Neil Zeghidour, Andrea Agostinelli
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed MusicRL system is the first to fine-tune a music generation model using human feedback, addressing the challenge of subjective musical preferences. By integrating continuous human feedback into post-deployment fine-tuning, MusicRL aims to improve the accuracy and adaptability of text-to-music models. The system uses reinforcement learning to maximize sequence-level rewards and designs reward functions related to text-adherence and audio quality. Human evaluations show that both MusicRL-R and MusicRL-U outperform the baseline, with the combined approach resulting in the best model according to human raters. Ablation studies reveal the importance of subjectivity in musical appreciation, highlighting the need for further human involvement in fine-tuning music generation models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MusicRL is a new way to make music computers. Right now, these machines can make songs that are okay, but they don’t really understand what people like or dislike about music. To fix this, scientists created MusicRL, which asks humans for feedback on the music it makes. This helps the computer learn what kind of music people like and how to make better songs. The system is good at making music that sounds like things people want to hear. It’s a big step forward in helping computers understand why we love certain types of music. |
Keywords
* Artificial intelligence * Fine tuning * Reinforcement learning
