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.

使用转移学习解决 Jazz 音乐中数据不足和乐曲自动生成的问题。实验使用两种转移学习方法，其中添加流派标签和使用流派分类器的模型比单独使用 Jazz 数据集更好，但不能充分利用流派不指定的数据集。