METEOR: Melody-aware Texture-controllable Symbolic Orchestral Music Generation via Transformer VAE
Dinh-Viet-Toan Le, Yi-Hsuan Yang

TL;DR
METEOR is a Transformer VAE model that enables melody-aware, texture-controllable re-orchestration of symbolic music, preserving melodic fidelity while allowing detailed texture and instrumentation control.
Contribution
It introduces a novel Transformer-based VAE for symbolic re-orchestration with controllable texture and melody preservation, outperforming existing style transfer models.
Findings
Outperforms style transfer models in quality and controllability
Achieves zero-shot orchestration comparable to specialized models
Maintains melodic fidelity with texture control
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…
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Taxonomy
TopicsMusic Technology and Sound Studies · Music and Audio Processing · Architecture and Computational Design
