Time-Shifted Token Scheduling for Symbolic Music Generation
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866912612660281344 |
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| author | Wang, Ting-Kang Tan, Chih-Pin Yang, Yi-Hsuan |
| author_facet | Wang, Ting-Kang Tan, Chih-Pin Yang, Yi-Hsuan |
| contents | Symbolic music generation faces a fundamental trade-off between efficiency and quality. Fine-grained tokenizations achieve strong coherence but incur long sequences and high complexity, while compact tokenizations improve efficiency at the expense of intra-token dependencies. To address this, we adapt a delay-based scheduling mechanism (DP) that expands compound-like tokens across decoding steps, enabling autoregressive modeling of intra-token dependencies while preserving efficiency. Notably, DP is a lightweight strategy that introduces no additional parameters and can be seamlessly integrated into existing representations. Experiments on symbolic orchestral MIDI datasets show that our method improves all metrics over standard compound tokenizations and narrows the gap to fine-grained tokenizations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_23749 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Time-Shifted Token Scheduling for Symbolic Music Generation Wang, Ting-Kang Tan, Chih-Pin Yang, Yi-Hsuan Machine Learning Symbolic music generation faces a fundamental trade-off between efficiency and quality. Fine-grained tokenizations achieve strong coherence but incur long sequences and high complexity, while compact tokenizations improve efficiency at the expense of intra-token dependencies. To address this, we adapt a delay-based scheduling mechanism (DP) that expands compound-like tokens across decoding steps, enabling autoregressive modeling of intra-token dependencies while preserving efficiency. Notably, DP is a lightweight strategy that introduces no additional parameters and can be seamlessly integrated into existing representations. Experiments on symbolic orchestral MIDI datasets show that our method improves all metrics over standard compound tokenizations and narrows the gap to fine-grained tokenizations. |
| title | Time-Shifted Token Scheduling for Symbolic Music Generation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.23749 |