Mixed-Initiative Generation of Multi-Channel Sequential Structures

We argue for the benefit of designing deep generative models through mixed-initiative combinations of deep learning algorithms and human specifications for authoring sequential content, such as stories and music.
Sequence models have shown increasingly convincing results in domains such as auto-completion, speech to text, and translation; however, longer-term structure remains a major challenge. Given lengthy inputs and outputs, deep generative systems still lack reliable representations of beginnings, middles, and ends, which are standard aspects of creating content in domains such as music composition. This paper aims to contribute a framework for mixed-initiative learning approaches, specifically for creative deep generative systems, and presents a case study of a deep generative model for music, Counterpoint by Convolutional Neural Network (Coconet).

• Human-Computer Interaction and Visualization

• Machine Intelligence