用自校正非时序自回归模型生成音乐
Generating Music with a Self-Correcting Non-Chronological Autoregressive Model
摘要：
我们描述了一种新的方法，用于生成音乐使用自校正，非时序，自回归模型。我们将音乐表示为一系列编辑事件，其中每个事件表示添加或删除一个音符-甚至是模型先前生成的音符。在推理过程中，我们使用直接祖先采样一次生成一个编辑事件。我们的方法允许模型修复以前的错误，例如不正确的采样笔记，并防止自回归模型容易出现的错误积累。另一个好处是在人类和人工智能协作作曲期间进行更精细的逐音符控制。我们通过定量指标和人类调查评估表明，我们的方法比无序NADE和吉布斯抽样方法产生更好的结果。
英文摘要：
We describe a novel approach for generating music using a self-correcting, non-chronological, autoregressive model. We represent music as a sequence of edit events, each of which denotes either the addition or removal of a note---even a note previously generated by the model. During inference, we generate one edit event at a time using direct ancestral sampling. Our approach allows the model to fix previous mistakes such as incorrectly sampled notes and prevent accumulation of errors which autoregressive models are prone to have. Another benefit is a finer, note-by-note control during human and AI collaborative composition. We show through quantitative metrics and human survey evaluation that our approach generates better results than orderless NADE and Gibbs sampling approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
