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arXiv · 2510.05881

Segment-Factorized Full-Song Generation on Symbolic Piano Music

Ping-Yi Chen·Chih-Pin Tan·Yi-Hsuan Yang
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Abstract

We propose the Segmented Full-Song Model (SFS) for symbolic full-song generation. The model accepts a user-provided song structure and an optional short seed segment that anchors the main idea around which the song is developed. By factorizing a song into segments and generating each one through selective attention to related segments, the model achieves higher quality and efficiency compared to prior work. To demonstrate its suitability for human-AI interaction, we further wrap SFS into a web application that enables users to iteratively co-create music on a piano roll with customizable structures and flexible ordering.

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Ping-Yi Chen, Chih-Pin Tan, Yi-Hsuan Yang. 2025-10-07. Segment-Factorized Full-Song Generation on Symbolic Piano Music. https://arxiv.org/abs/2510.05881

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