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Douglas Eck

Alumni

Publications

Counterpoint by Convolution
Cheng-Zhi Anna Huang
Tim Cooijmans
Adam Roberts
Aaron Courville
Douglas Eck
Machine learning models of music typically break down the task of composition into a chronological process, composing a piece of music in a … (voir plus)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. We explore the use of blocked Gibbs sampling as an analogue to the human approach, and introduce Coconet, a convolutional neural network in the NADE family of generative models. Despite ostensibly sampling from the same distribution as the NADE ancestral sampling procedure, we find that a blocked Gibbs approach significantly improves sample quality. We provide evidence that this is 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. We demonstrate the versatility of our method on unconditioned polyphonic music generation.
2019-03-17
ArXiv (prépublication)
openreview.net
openreview.net