METEOR: Melody-aware Texture-controllable Symbolic Orchestral Music Generation via Transformer VAE
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866909666055815168 |
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| author | Le, Dinh-Viet-Toan Yang, Yi-Hsuan |
| author_facet | Le, Dinh-Viet-Toan Yang, Yi-Hsuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_11753 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | METEOR: Melody-aware Texture-controllable Symbolic Orchestral Music Generation via Transformer VAE Le, Dinh-Viet-Toan Yang, Yi-Hsuan Sound Audio and Speech Processing 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. |
| title | METEOR: Melody-aware Texture-controllable Symbolic Orchestral Music Generation via Transformer VAE |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2409.11753 |