Incorporating Music Knowledge in Continual Dataset Augmentation for Music Generation
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.
ArXiv