Unifying Multitrack Music Arrangement via Reconstruction Fine-Tuning and Efficient Tokenization

Abstract

Automatic music arrangement streamlines the creation of musical variants for composers and arrangers, reducing reliance on extensive music expertise. However, existing methods suffer from inefficient tokenization, underutilization of pre-trained music language models (LMs), and suboptimal fidelity and coherence in generated arrangements. This paper introduces an efficient multitrack music tokenizer for unconditional and conditional symbolic music generation, along with a unified sequence-to-sequence reconstruction fine-tuning objective for pre-trained music LMs that balances task-specific needs with coherence constraints. Our approach achieves state-of-the-art results on band arrangement, piano reduction, and drum arrangement, surpassing task-specific models in both objective metrics and perceptual quality. Additionally, we demonstrate that generative pretraining significantly contributes to the performance across these arrangement tasks, especially when handling long segments with complex alignment.

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Reproductions