Segment-Factorized Full-Song Generation on Symbolic Piano Music

Ping-Yi Chen, Chih-Pin Tan, Yi-Hsuan Yang

Keywords: symbolic music generation, machine learning, music segmentation

TL;DR: The paper proposes a full-song generation model that leverages selective context and is conditioned on a user-provided song structure and seed segment.

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.

Track: Paper Track

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Submission Number: 25