论文标题
通过神经机器翻译建模巴洛克式两部分对立面
Modeling Baroque Two-Part Counterpoint with Neural Machine Translation
论文作者
论文摘要
我们提出了一个基于神经机器翻译（NMT）范式的系统生成的系统。我们认为巴洛克式的对位，并有兴趣建模任何两个给定零件之间的相互作用作为给定源材料与适当目标材料之间的映射。像翻译一样，前者对后者施加了一些限制，但并未完全定义它。我们整理和编辑了巴洛克式零件的定制数据集，使用它来训练基于注意力的神经网络模型，并通过BLEU分数和音乐学分析评估产生的输出。我们表明，我们的模型能够以一些惯用的商标做出反应，例如模仿和适当的节奏偏移，尽管它没有学会在风格上正确正确的逆向运动（例如，避免平行五分之一）或更严格的模仿规则，例如佳能。
We propose a system for contrapuntal music generation based on a Neural Machine Translation (NMT) paradigm. We consider Baroque counterpoint and are interested in modeling the interaction between any two given parts as a mapping between a given source material and an appropriate target material. Like in translation, the former imposes some constraints on the latter, but doesn't define it completely. We collate and edit a bespoke dataset of Baroque pieces, use it to train an attention-based neural network model, and evaluate the generated output via BLEU score and musicological analysis. We show that our model is able to respond with some idiomatic trademarks, such as imitation and appropriate rhythmic offset, although it falls short of having learned stylistically correct contrapuntal motion (e.g., avoidance of parallel fifths) or stricter imitative rules, such as canon.
