Unlocking Potential in Pre-Trained Music Language Models for Versatile Multi-Track Music Arrangement
<|reference_start|>Unlocking Potential in Pre-Trained Music Language Models for Versatile Multi-Track Music Arrangement: 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.<|reference_end|>
@article{ou2024unlocking,
    title={Unlocking Potential in Pre-Trained Music Language Models for Versatile
  Multi-Track Music Arrangement},
    author={Longshen Ou, Jingwei Zhao, Ziyu Wang, Gus Xia, Ye Wang},
    journal={arXiv preprint arXiv:2408.15176},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.15176},
    primaryClass={cs.SD cs.CL eess.AS}
}