@inproceedings{wang-etal-2026-segtune,
title = "{S}eg{T}une: Structured and Fine-Grained Control for Song Generation",
author = "Wang, Yuejiao and
Ji, Zihao and
Cai, Pengfei and
Li, Xu and
Zheng, Haorui and
Song, Zewen and
Liu, Zhongliang and
Zhang, Chen and
Wan, Pengfei",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.586/",
pages = "12883--12897",
ISBN = "979-8-89176-390-6",
abstract = "Recent advances in neural song generation have enabled high-quality synthesis from lyrics and global textual prompts. However, most systems fail to model temporally varying attributes of songs, severely limiting fine-grained control over musical structure and dynamics. To address this, we propose Segtune, a Diffusion Transformer-based framework enabling structured and fine-grained controllability by allowing users or large language models (LLMs) to specify local musical descriptions aligned to song segments. These segment prompts are temporally broadcast to corresponding time windows, while global prompts ensure stylistic coherence. To support precise lyric-to-music alignment, we introduce an LLM-based duration predictor that autoregressively generates sentence-level timestamps in LyRiCs format. We further construct a large-scale data pipeline for high-quality song collection with aligned lyrics and prompts, and propose new metrics to evaluate segment alignment and vocal consistency. Experiments demonstrate that Segtune outperforms existing baselines in both musicality and controllability. Visit our demo page for codes and more generated songs."
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<abstract>Recent advances in neural song generation have enabled high-quality synthesis from lyrics and global textual prompts. However, most systems fail to model temporally varying attributes of songs, severely limiting fine-grained control over musical structure and dynamics. To address this, we propose Segtune, a Diffusion Transformer-based framework enabling structured and fine-grained controllability by allowing users or large language models (LLMs) to specify local musical descriptions aligned to song segments. These segment prompts are temporally broadcast to corresponding time windows, while global prompts ensure stylistic coherence. To support precise lyric-to-music alignment, we introduce an LLM-based duration predictor that autoregressively generates sentence-level timestamps in LyRiCs format. We further construct a large-scale data pipeline for high-quality song collection with aligned lyrics and prompts, and propose new metrics to evaluate segment alignment and vocal consistency. Experiments demonstrate that Segtune outperforms existing baselines in both musicality and controllability. Visit our demo page for codes and more generated songs.</abstract>
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%0 Conference Proceedings
%T SegTune: Structured and Fine-Grained Control for Song Generation
%A Wang, Yuejiao
%A Ji, Zihao
%A Cai, Pengfei
%A Li, Xu
%A Zheng, Haorui
%A Song, Zewen
%A Liu, Zhongliang
%A Zhang, Chen
%A Wan, Pengfei
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F wang-etal-2026-segtune
%X Recent advances in neural song generation have enabled high-quality synthesis from lyrics and global textual prompts. However, most systems fail to model temporally varying attributes of songs, severely limiting fine-grained control over musical structure and dynamics. To address this, we propose Segtune, a Diffusion Transformer-based framework enabling structured and fine-grained controllability by allowing users or large language models (LLMs) to specify local musical descriptions aligned to song segments. These segment prompts are temporally broadcast to corresponding time windows, while global prompts ensure stylistic coherence. To support precise lyric-to-music alignment, we introduce an LLM-based duration predictor that autoregressively generates sentence-level timestamps in LyRiCs format. We further construct a large-scale data pipeline for high-quality song collection with aligned lyrics and prompts, and propose new metrics to evaluate segment alignment and vocal consistency. Experiments demonstrate that Segtune outperforms existing baselines in both musicality and controllability. Visit our demo page for codes and more generated songs.
%U https://aclanthology.org/2026.acl-long.586/
%P 12883-12897
Markdown (Informal)
[SegTune: Structured and Fine-Grained Control for Song Generation](https://aclanthology.org/2026.acl-long.586/) (Wang et al., ACL 2026)
ACL
- Yuejiao Wang, Zihao Ji, Pengfei Cai, Xu Li, Haorui Zheng, Zewen Song, Zhongliang Liu, Chen Zhang, and Pengfei Wan. 2026. SegTune: Structured and Fine-Grained Control for Song Generation. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12883–12897, San Diego, California, United States. Association for Computational Linguistics.