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.
MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment
Research Center for Information Technology Innovation, Academia Sinica（中央研究院信息技术创新研究中心）；Department of Computer Science, National Tsing Hua University（国立清华大学计算机科学系）
A Framework for Automated Pop-song Melody Generation with Piano Accompaniment Arrangement
New York University Shanghai（上海纽约大学）；Fudan University（复旦大学）
我们贡献了一个流行歌曲自动化框架的主旋律生成和伴奏安排。该框架反映了人类音乐创作的主要程序，通过统一的策略生成主旋律和钢琴伴奏。具体来说，我们以和弦进行为输入，并提出了三种模型来生成一个结构化的旋律与钢琴伴奏纹理。首先，和声交替模型将原始输入和弦进行变换为经改变的和弦进行，以更好地适应指定的音乐风格。第二，旋律生成模型使用季节性ARMA（自回归移动平均）过程生成伴奏的主旋律和其他声音（旋律线）。第三，旋律整合模式将旋律线（人声）整合在一起作为最终的钢琴伴奏。我们使用主观听力测试评估所提出的框架。实验结果表明，生成的旋律评分明显高于双向LSTM生成的旋律，我们的伴奏编排结果与最先进的商业软件Band in a Box相当。
SongDriver: Real-time Music Accompaniment Generation without Logical Latency nor Exposure Bias
Zhejiang University（浙江大学）；Alibaba-Zhejiang University Joint Institute of Frontier Technologies（阿里巴巴-浙江大学前沿技术联合研究院）；Jingchu University of Technology（荆楚理工学院）；Shandong University（山东大学）
Structured Multi-Track Accompaniment Arrangement via Style Prior Modelling
Institute of Data Science, NUS（新加坡国立大学数据科学研究院）；Integrative Sciences and Engineering Programme, NUS Graduate School（新加坡国立大学研究生院整合科学与工程项目）；Machine Learning Department, MBZUAI（穆罕默德·本·扎耶德人工智能大学机器学习系）；Computer Science Department, NYU Shanghai（上海纽约大学计算机科学系）；School of Computing, NUS（新加坡国立大学计算机学院）
On Every Note a Griff: Looking for a Useful Representation of Basso Continuo Performance Style
Faculty of Mathematics and Physics（数学与物理系）；Charles University（查尔斯大学）；Institute of Computational Perception（计算感知研究所）；Johannes Kepler University（约翰·凯撒大学）
Unifying Symbolic Music Arrangement: Track-Aware Reconstruction and Structured Tokenization
Sound and Music Computing Lab, School of Computing, NUS（新加坡国立大学计算机学院声音与音乐计算实验室）；Courant Institute of Mathematical Sciences, New York University（纽约大学应用数学科学学院）；Music X Lab, MBZUAI（MBZUAI音乐X实验室）
