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arXiv · 2006.13331

Incorporating Music Knowledge in Continual Dataset Augmentation for Music Generation

Alisa Liu·Alexander Fang·Gaëtan Hadjeres·Prem Seetharaman·Bryan Pardo
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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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Alisa Liu, Alexander Fang, Gaëtan Hadjeres, Prem Seetharaman, Bryan Pardo. 2020-06-23. Incorporating Music Knowledge in Continual Dataset Augmentation for Music Generation. https://arxiv.org/abs/2006.13331

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