Towards Mixed-Initiative Generation of Multi-Channel Sequential Structure

Huang, Anna, Chen, Sherol, Nelson, Mark and Eck, Douglas (2018) Towards Mixed-Initiative Generation of Multi-Channel Sequential Structure. In: International Conference on Learning Representations, 30 Apr - 3 May 2018, Vancouver, Canada.

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Abstract / Summary

We argue for the benefit of designing deep generative models through a mixed-initiative, co-creative combination of deep learning algorithms and human specifications, focusing on multi-channel music composition. Sequence models have shown convincing results in domains such as summarization 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 generation approaches that let humans both supply and control some of these aspects in deep generative models for music, and present a case study of Counterpoint by Convolutional Neural Network (CoCoNet).

Item Type: Conference or Workshop Item (Paper)
Subjects: Computer Science, Information & General Works
Music > Digital Music
Courses by Department: The Games Academy > Computing for Games
Related URLs:
Depositing User: Mark Nelson
Date Deposited: 23 Apr 2018 15:26
Last Modified: 11 Nov 2022 16:29


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