用风格规划与模式检索生成结构感知的钢琴伴奏。
Structure-Aware Piano Accompaniment via Style Planning and Dataset-Aligned Pattern Retrieval
- 分两步生成：先用轻量Transformer生成每小节风格计划，再从真实演奏中检索匹配模式。
- 生成的伴奏在长段落中保持风格一致且多样化，符合结构和和声要求。
- 适合音乐生成研究者或需要高质量伴奏的创作者使用。
我们提出一种符号化钢琴伴奏的结构感知生成方法，将高层规划与低层实现解耦。采用轻量级Transformer模型，基于段落/乐句结构和功能和声，预测可解释的每小节风格计划；随后通过检索器从真实演奏语料库中选取并重新和声化钢琴模式。我们将检索建模为显式能量函数下的模式匹配，包含和声可行性、结构角色适配性、声部进行连续性、风格偏好及重复控制等项。给定结构化主旋律谱（lead sheet）和可选关键词提示，系统可生成钢琴伴奏MIDI文件。实验表明，由风格规划引导的检索能生成风格实现强、多样性高的长时序伴奏。进一步分析显示，不同风格间具有明显隔离性，验证了该推理时方法的有效性。
原文摘要 · Abstract (English)
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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