Counterpoint by Convolution
Counterpoint by Convolution
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发表时间：
2019-03
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通讯作者：
Cheng-Zhi Anna Huang;Tim Cooijmans;Adam Roberts;Aaron C. Courville;D. Eck
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作者：
Cheng-Zhi Anna Huang;Tim Cooijmans;Adam Roberts;Aaron C. Courville;D. Eck
音乐的机器学习模型通常将构图的任务分解为按时间顺序排列的过程，从头到尾都可以在单个传球中构成一段音乐。相反，人类作曲家以非线性的方式撰写音乐，到处都在散布图案，通常会重新审视以前做出的选择。为了更好地近似此过程，我们训练一个卷积神经网络完成部分音乐分数，并探索使用阻塞的吉布斯采样作为重写的类似物。模型和生成过程均未与特定的因果关系方向相关。我们的模型是无秩序的实例（Uria等，2014），它允许更直接的祖先采样。但是，我们发现吉布斯采样大大提高了样本质量，我们证明这是由于某些条件分布的建模不佳所致。此外，我们表明，即使廉价的近似吉布斯（Gibbs）程序也来自Yao等人。 （2014年）基于对数可能和人类评估，比祖先采样得出的样本更好。
Machine learning models of music typically break up the task of composition into a chronological process, composing a piece of music in a single pass from beginning to end. On the contrary, human composers write music in a nonlinear fashion, scribbling motifs here and there, often revisiting choices previously made. In order to better approximate this process, we train a convolutional neural network to complete partial musical scores, and explore the use of blocked Gibbs sampling as an analogue to rewriting. Neither the model nor the generative procedure are tied to a particular causal direction of composition. Our model is an instance of orderless NADE (Uria et al., 2014), which allows more direct ancestral sampling. However, we find that Gibbs sampling greatly improves sample quality, which we demonstrate to be due to some conditional distributions being poorly modeled. Moreover, we show that even the cheap approximate blocked Gibbs procedure from Yao et al. (2014) yields better samples than ancestral sampling, based on both log-likelihood and human evaluation.