Unlocking Potential in Pre-Trained Music Language Models for Versatile Multi-Track Music Arrangement

• URL: http://arxiv.org/abs/2408.15176v1
• Date: Tue, 27 Aug 2024 16:18:51 GMT
• Title: Unlocking Potential in Pre-Trained Music Language Models for Versatile Multi-Track Music Arrangement
• Authors: Longshen Ou, Jingwei Zhao, Ziyu Wang, Gus Xia, Ye Wang,
• Abstract summary: We propose a unified sequence-to-sequence framework that enables the fine-tuning of a symbolic music language model. Our experiments demonstrate that the proposed approach consistently achieves higher musical quality compared to task-specific baselines.
• Score: 10.714947060480426
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models have shown significant capabilities across various domains, including symbolic music generation. However, leveraging these pre-trained models for controllable music arrangement tasks, each requiring different forms of musical information as control, remains a novel challenge. In this paper, we propose a unified sequence-to-sequence framework that enables the fine-tuning of a symbolic music language model for multiple multi-track arrangement tasks, including band arrangement, piano reduction, drum arrangement, and voice separation. Our experiments demonstrate that the proposed approach consistently achieves higher musical quality compared to task-specific baselines across all four tasks. Furthermore, through additional experiments on probing analysis, we show the pre-training phase equips the model with essential knowledge to understand musical conditions, which is hard to acquired solely through task-specific fine-tuning.

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