论文标题
具有神经离散表示形式的符号音乐循环产生
Symbolic Music Loop Generation with Neural Discrete Representations
论文作者
论文摘要
由于大多数音乐具有从主题到短语的重复结构，因此重复的音乐想法可能是音乐创作的基本操作。我们关注的基本块概念化为循环，这是音乐的必要成分。此外，有意义的音符模式可以在有限的空间中形成，因此足以用其他域中的离散符号组合来表示它们。在这项工作中，我们通过学习离散表示提出符号音乐循环生成。我们首先使用循环检测器从MIDI数据集提取循环，然后学习通过提取回路的离散潜在代码训练的自回归模型。我们表明，我们的模型在忠诚度和多样性方面都优于众所周知的音乐生成模型，并评估随机空间。我们的代码和补充材料可从https://github.com/sjhan91/loop_vqvae_official获得。
Since most of music has repetitive structures from motifs to phrases, repeating musical ideas can be a basic operation for music composition. The basic block that we focus on is conceptualized as loops which are essential ingredients of music. Furthermore, meaningful note patterns can be formed in a finite space, so it is sufficient to represent them with combinations of discrete symbols as done in other domains. In this work, we propose symbolic music loop generation via learning discrete representations. We first extract loops from MIDI datasets using a loop detector and then learn an autoregressive model trained by discrete latent codes of the extracted loops. We show that our model outperforms well-known music generative models in terms of both fidelity and diversity, evaluating on random space. Our code and supplementary materials are available at https://github.com/sjhan91/Loop_VQVAE_Official.
