Incorporating Music Knowledge in Continual Dataset Augmentation for Music Generation

• URL: http://arxiv.org/abs/2006.13331v4
• Date: Mon, 20 Jul 2020 20:56:59 GMT
• Title: Incorporating Music Knowledge in Continual Dataset Augmentation for Music Generation
• Authors: Alisa Liu, Alexander Fang, Ga\"etan Hadjeres, Prem Seetharaman, Bryan Pardo
• Abstract summary: Aug-Gen is a method of dataset augmentation for any music generation system trained on a resource-constrained domain. We apply Aug-Gen to Transformer-based chorale generation in the style of J.S. Bach, and show that this allows for longer training and results in better generative output.
• Score: 69.06413031969674
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning has rapidly become the state-of-the-art approach for music generation. However, training a deep model typically requires a large training set, which is often not available for specific musical styles. In this paper, we present augmentative generation (Aug-Gen), a method of dataset augmentation for any music generation system trained on a resource-constrained domain. The key intuition of this method is that the training data for a generative system can be augmented by examples the system produces during the course of training, provided these examples are of sufficiently high quality and variety. We apply Aug-Gen to Transformer-based chorale generation in the style of J.S. Bach, and show that this allows for longer training and results in better generative output.

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