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。