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arXiv · 2610.31510

Piano2Pop: Expanding Piano Demos into Pop Productions Without Aligned Supervision

Yun-Chen Cheng·Chih-Pin Tan·Tzu-Hung Huang·Yi-Hsuan Yang
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Abstract

We introduce Piano2Pop, a music-expansion task that transforms piano MIDI into instrumental pop productions. Enabled by recent advances in music generation and polyphonic music transcription, our pseudo-supervised approach conditions a pretrained flow-matching model on piano rolls without aligned piano-pop supervision. We train on synthetic piano-instrumental pairs derived from pop recordings, comparing pair-free transcription, including function-selective (FST) and register-density (RDA) augmentations, with a pair-supervised piano-cover generator as pseudo-demo sources. On 476 testing real-world piano-cover/pop pairs, the pair-supervised approach achieves the best Frechet Audio Distance, Accompaniment Prompt Adherence, and spectral shape on real-piano inputs, while FST mitigates piano-heavy outputs in pair-free training and remains a promising alternative. All fine-tuned systems significantly outperform the zero-shot baselines, which suggests a role for task-specific modeling.

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Yun-Chen Cheng, Chih-Pin Tan, Tzu-Hung Huang, Yi-Hsuan Yang. 2026-10-22. Piano2Pop: Expanding Piano Demos into Pop Productions Without Aligned Supervision. https://arxiv.org/abs/2610.31510

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