Arrange, Inpaint, and Refine: Steerable Long-term Music Audio Generation and Editing via Content-based Controls
CoRR(2024)
Abstract
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, we introduce a novel
Parameter-Efficient Fine-Tuning (PEFT) method. This approach enables
autoregressive language models to seamlessly address music inpainting tasks.
Additionally, our PEFT 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. A demo
page[ .] showcasing our work and
source codes[.] are available
online.
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