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

• URL: http://arxiv.org/abs/2402.09508v3
• Date: Sun, 06 Oct 2024 21:26:48 GMT
• Title: Arrange, Inpaint, and Refine: Steerable Long-term Music Audio Generation and Editing via Content-based Controls
• Authors: Liwei Lin, Gus Xia, Yixiao Zhang, Junyan Jiang,
• Abstract summary: Large Language Models (LLMs) have shown promise in generating high-quality music, but their focus on autoregressive generation limits their utility in music editing tasks. We propose a novel approach leveraging a parameter-efficient heterogeneous adapter combined with a masking training scheme. Our method integrates frame-level content-based controls, facilitating track-conditioned music refinement and score-conditioned music arrangement.
• Score: 6.176747724853209
• License:
• 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 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.

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