Abstract
Melodic harmonization, the task of generating harmonic accompaniments for a given melody, remains a central challenge in computational music generation. Recent single encoder transformer approaches have framed harmonization as a masked sequence modeling problem, but existing training curricula inspired by discrete diffusion often result in weak (cross) attention between melody and harmony. This leads to limited exploitation of melodic cues, particularly in out-of-domain contexts. In this work, we introduce a training curriculum, FF (full-to-full), which keeps all harmony tokens masked for several training steps before progressively unmasking entire sequences during training to strengthen melody-harmony interactions. We systematically evaluate this approach against prior curricula across multiple experimental axes, including temporal quantization (quarter vs. sixteenth note), bar-level vs. time-signature conditioning, melody representation (full range vs. pitch class), and inference-time unmasking strategies. Models are trained on the HookTheory dataset and evaluated both in-domain and on a curated collection of jazz standards, using a comprehensive set of metrics that assess chord progression structure, harmony-melody alignment, and rhythmic coherence. Results demonstrate that the proposed FF curriculum consistently outperforms baselines in nearly all metrics, with particularly strong gains in out-of-domain evaluations where harmonic adaptability to novel melodic queues is crucial. We further find that quarter-note quantization, intertwining of bar tokens, and pitch-class melody representations are advantageous in the FF setting. Our findings highlight the importance of training curricula in enabling effective melody conditioning and suggest that full-to-full unmasking offers a robust strategy for single encoder harmonization.
Abstract (translated)
旋律和声化，即为给定的旋律生成和声伴奏，在计算音乐生成中仍然是一个核心挑战。最近采用单一编码器变压器的方法将和声化问题视为屏蔽序列建模问题，但现有的训练课程（受离散扩散启发）通常会导致旋律与和声之间的弱交叉注意力。这导致了对旋律线索利用的限制，尤其是在域外上下文的情况下。 在这项工作中，我们引入了一种训练课程 FF (full-to-full)，该方法在训练初期将所有和声音符保持屏蔽状态，并逐渐在整个序列训练过程中解除屏蔽，以加强旋律与和声之间的相互作用。我们在多个实验轴上系统地评估了这种方法与先前的课程效果，包括时间量化（四分音符 vs. 十六分音符）、小节级 vs. 节拍签名条件、旋律表示形式（全范围 vs. 音阶）以及推理时的解除屏蔽策略。模型在 HookTheory 数据集上进行训练，并且使用全面评估和声进程结构、和声-旋律对齐以及节奏一致性的指标，在域内及一个精选的爵士标准曲集合中进行了评估。 实验结果表明，我们提出的 FF 课程方案几乎在所有指标上都优于基线方法，特别是在需要适应新型旋律线索的域外评估中表现尤为突出。此外，四分音符量化、小节标记交织以及音阶表示形式被证明在 FF 设置下具有优势。我们的研究强调了训练课程在有效旋律调适中的重要性，并表明全面解除屏蔽策略为单一编码器和声生成提供了一种稳健的方法。
URL
https://arxiv.org/abs/2601.16150
