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.

翻译：我们正在探索利用基于人工智能的生成式音乐系统为音乐信息检索（MIR）任务生成训练数据的更广泛概念。为启动这一研究方向，我们开展了一项初步实验：基于由MusicGen创建的纯人工音乐数据集训练了一个音乐流派分类器。我们构建了超过50,000条带流派条件的文本描述，并生成了一个涵盖五种音乐流派的音乐片段集合。初步结果表明，该模型能够从人工音乐曲目中学习到具有流派特定特征的知识，并良好地泛化至真实世界的音乐录音。