Arrange, Inpaint, and Refine: Steerable Long-term Music Audio Generation and Editing via Content-based Controls

Arrange, Inpaint, and Refine: Steerable Long-term Music Audio Generation and Editing via Content-based Controls

Liwei Lin, Gus Xia, Yixiao Zhang, Junyan Jiang

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
AI, Arts & Creativity. Pages 7690-7698. https://doi.org/10.24963/ijcai.2024/851

Controllable music generation plays a vital role in human-AI music co-creation. While Large Language Models (LLMs) have shown promise in generating high-quality music, their focus on autoregressive generation limits their utility in music editing tasks. To bridge this gap, To address this gap, we propose a novel approach leveraging a parameter-efficient heterogeneous adapter combined with a masking training scheme. This approach enables autoregressive language models to seamlessly address music inpainting tasks. Additionally, our method integrates frame-level content-based controls, facilitating track-conditioned music refinement and score-conditioned music arrangement. We apply this method to fine-tune MusicGen, a leading autoregressive music generation model. Our experiments demonstrate promising results across multiple music editing tasks, offering more flexible controls for future AI-driven music editing tools. The source codes and a demo page showcasing our work are available at https://kikyo-16.github.io/AIR.
Keywords:
Application domains: Music and sound
Methods and resources: Machine learning, deep learning, neural models, reinforcement learning