We are investigating the broader concept of using AI-based generative music
systems to generate training data for Music Information Retrieval (MIR) tasks.
To kick off this line of work, we ran an initial experiment in which we trained
a genre classifier on a fully artificial music dataset created with MusicGen.
We constructed over 50 000 genre- conditioned textual descriptions and
generated a collection of music excerpts that covers five musical genres. Our
preliminary results show that the proposed model can learn genre-specific
characteristics from artificial music tracks that generalise well to real-world
music recordings.

通过使用基于人工智能的生成音乐系统为音乐信息检索（MIR）任务生成训练数据，我们探讨了更广泛的概念。为了启动这个研究方向，我们进行了一项初步实验，利用 MusicGen 创建了一个完全人工的音乐数据集，在此基础上我们训练了一个流派分类器。我们制作了超过 50,000 个以流派为条件的文本描述，并生成了一系列覆盖五种音乐流派的音乐片段。我们的初步结果表明，所提出的模型能够从人工音乐曲目中学习到流派特定的特征，并在现实世界的音乐录音中推广得很好。

音乐生成是否能够为 MIR 任务创建训练数据？

Can MusicGen Create Training Data for MIR Tasks?

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。