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.

翻译：本文提出一种结构感知的符号化钢琴伴奏方法，将高层规划与音符级实现解耦。我们采用轻量级Transformer模型，基于乐段/乐句结构与功能和声条件，预测可解释的逐小节风格规划；随后通过检索器从语料库中选取人类演奏的钢琴模式并进行重和声处理。我们将检索过程形式化为显式能量函数约束下的模式匹配问题，该能量函数包含和声可行性、结构角色兼容性、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及可选的关键词提示，系统可生成钢琴伴奏MIDI文件。实验表明，基于Transformer风格规划器的引导检索能够生成风格表现力强且多样化的长篇幅伴奏。我们进一步分析了规划器的消融实验，并量化了风格间的隔离度。实验结果验证了本推理时方法在钢琴伴奏生成任务中的有效性。