  In this paper, we tackle the problem of transfer learning for Jazz automatic
generation. Jazz is one of representative types of music, but the lack of Jazz
data in the MIDI format hinders the construction of a generative model for
Jazz. Transfer learning is an approach aiming to solve the problem of data
insufficiency, so as to transfer the common feature from one domain to another.
In view of its success in other machine learning problems, we investigate
whether, and how much, it can help improve automatic music generation for
under-resourced musical genres. Specifically, we use a recurrent variational
autoencoder as the generative model, and use a genre-unspecified dataset as the
source dataset and a Jazz-only dataset as the target dataset. Two transfer
learning methods are evaluated using six levels of source-to-target data
ratios. The first method is to train the model on the source dataset, and then
fine-tune the resulting model parameters on the target dataset. The second
method is to train the model on both the source and target datasets at the same
time, but add genre labels to the latent vectors and use a genre classifier to
improve Jazz generation. The evaluation results show that the second method
seems to perform better overall, but it cannot take full advantage of the
genre-unspecified dataset.
