Attention-based Transformer models have been increasingly employed for
automatic music generation. To condition the generation process of such a model
with a user-specified sequence, a popular approach is to take that conditioning
sequence as a priming sequence and ask a Transformer decoder to generate a
continuation. However, this prompt-based conditioning cannot guarantee that the
conditioning sequence would develop or even simply repeat itself in the
generated continuation. In this paper, we propose an alternative conditioning
approach, called theme-based conditioning, that explicitly trains the
Transformer to treat the conditioning sequence as a thematic material that has
to manifest itself multiple times in its generation result. This is achieved
with two main technical contributions. First, we propose a deep learning-based
approach that uses contrastive representation learning and clustering to
automatically retrieve thematic materials from music pieces in the training
data. Second, we propose a novel gated parallel attention module to be used in
a sequence-to-sequence (seq2seq) encoder/decoder architecture to more
effectively account for a given conditioning thematic material in the
generation process of the Transformer decoder. We report on objective and
subjective evaluations of variants of the proposed Theme Transformer and the
conventional prompt-based baseline, showing that our best model can generate,
to some extent, polyphonic pop piano music with repetition and plausible
variations of a given condition.

本文提出了一种基于主题的条件控制机制，通过深度学习的对比学习和聚类技术自动提取音乐片段中的主题材料，并在序列到序列编码器 / 解码器结构中使用了一种新的门控并行注意力模块来更有效地考虑给定的感应主题材料，以生成具有重复和合理变化的多声部流行钢琴音乐。