论文标题
流行音乐变压器：基于节拍的建模和产生表现力的流行钢琴作品
Pop Music Transformer: Beat-based Modeling and Generation of Expressive Pop Piano Compositions
论文作者
论文摘要
最近已经提出了许多基于深度学习的模型，用于自动音乐构图。在这些模型中，变压器是一种突出的方法，是产生表现力的经典钢琴性能，其连贯的结构长达一分钟。该模型很强大，因为它可以自己学习数据抽象，而没有太多的人为域知识或约束。与这种一般方法相反，本文表明，当我们改善音乐得分转换为馈送到变压器模型的数据的方式时，变形金刚可以做得更好。特别是，我们试图在输入数据中强加一个度量结构，以便更容易地了解音乐中的节拍 - 键型层次结构。新的数据表示保持了局部节奏变化的灵活性，并提供了控制音乐节奏和谐波结构的障碍。通过这种方法，我们构建了一个流行音乐变压器，它比现有的变压器模型构成具有更好节奏结构的流行钢琴音乐。
A great number of deep learning based models have been recently proposed for automatic music composition. Among these models, the Transformer stands out as a prominent approach for generating expressive classical piano performance with a coherent structure of up to one minute. The model is powerful in that it learns abstractions of data on its own, without much human-imposed domain knowledge or constraints. In contrast with this general approach, this paper shows that Transformers can do even better for music modeling, when we improve the way a musical score is converted into the data fed to a Transformer model. In particular, we seek to impose a metrical structure in the input data, so that Transformers can be more easily aware of the beat-bar-phrase hierarchical structure in music. The new data representation maintains the flexibility of local tempo changes, and provides hurdles to control the rhythmic and harmonic structure of music. With this approach, we build a Pop Music Transformer that composes Pop piano music with better rhythmic structure than existing Transformer models.
