Structure-Aware Piano Accompaniment via Style Planning and Dataset-Aligned Pattern Retrieval

• URL: http://arxiv.org/abs/2602.15074v1
• Date: Mon, 16 Feb 2026 03:54:34 GMT
• Title: Structure-Aware Piano Accompaniment via Style Planning and Dataset-Aligned Pattern Retrieval
• Authors: Wanyu Zang, Yang Yu, Meng Yu,
• Abstract summary: We introduce a structure-aware approach for symbolic piano accompaniment.<n>A transformer predicts an interpretable, per-measure style plan conditioned on section/phrase structure and functional harmony.<n>A retriever selects and reharmonizes human-performed piano patterns from a corpus.
• Score: 8.505620355469725
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a structure-aware approach for symbolic piano accompaniment that decouples high-level planning from note-level realization. A lightweight transformer predicts an interpretable, per-measure style plan conditioned on section/phrase structure and functional harmony, and a retriever then selects and reharmonizes human-performed piano patterns from a corpus. We formulate retrieval as pattern matching under an explicit energy with terms for harmonic feasibility, structural-role compatibility, voice-leading continuity, style preferences, and repetition control. Given a structured lead sheet and optional keyword prompts, the system generates piano-accompaniment MIDI. In our experiments, transformer style-planner-guided retrieval produces diverse long-form accompaniments with strong style realization. We further analyze planner ablations and quantify inter-style isolation. Experimental results demonstrate the effectiveness of our inference-time approach for piano accompaniment generation.

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