论文标题
通过自我校正的非儿童学自学模型产生音乐
Generating Music with a Self-Correcting Non-Chronological Autoregressive Model
论文作者
论文摘要
我们描述了一种新颖的方法，可以使用自我校正，非平均学，自回归模型来产生音乐。我们表示音乐是一系列编辑事件，每个事件都表示添加或删除音符 - 甚至是模型先前生成的注释。在推断期间，我们一次使用直接祖传抽样一次生成一个编辑事件。我们的方法使该模型可以解决以前的错误，例如错误采样的笔记，并防止误差的积累，这些错误易于进行自回归模型。另一个好处是在人类和AI协作组成期间进行精细的，注释的控制。我们通过定量指标和人类调查评估表明，我们的方法比无秩序的Nade和Gibbs采样方法产生更好的结果。
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.
