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arXiv · 2409.11753

METEOR: Melody-aware Texture-controllable Symbolic Orchestral Music Generation via Transformer VAE

Dinh-Viet-Toan Le·Yi-Hsuan Yang
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Abstract

Re-orchestration is the process of adapting a music piece for a different set of instruments. By altering the original instrumentation, the orchestrator often modifies the musical texture while preserving a recognizable melodic line and ensures that each part is playable within the technical and expressive capabilities of the chosen instruments. In this work, we propose METEOR, a model for generating Melody-aware Texture-controllable re-Orchestration with a Transformer-based variational auto-encoder (VAE). This model performs symbolic instrumental and textural music style transfers with a focus on melodic fidelity and controllability. We allow bar- and track-level controllability of the accompaniment with various textural attributes while keeping a homophonic texture. With both subjective and objective evaluations, we show that our model outperforms style transfer models on a re-orchestration task in terms of generation quality and controllability. Moreover, it can be adapted for a lead sheet orchestration task as a zero-shot learning model, achieving performance comparable to a model specifically trained for this task.

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Dinh-Viet-Toan Le, Yi-Hsuan Yang. 2024-09-18. METEOR: Melody-aware Texture-controllable Symbolic Orchestral Music Generation via Transformer VAE. https://arxiv.org/abs/2409.11753

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