Music Genres serve as an important meta-data in the field of music information retrieval and have been widely used for music classification and analysis tasks. Visualizing these music genres can thus be helpful for music exploration, archival and recommendation. Probabilistic topic models have been very successful in modelling text documents. In this work, we visualize music genres using a probabilistic topic model. Unlike text documents, audio is continuous and needs to be sliced into smaller segments. We use simple MFCC features of these segments as musical words. We apply the topic model on the corpus and subsequently use the genre annotations of the data to interpret and visualize the latent space.

中文翻译：

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使用主题模型可视化音乐流派

音乐流派是音乐信息检索领域中的重要元数据，已被广泛用于音乐分类和分析任务。因此，可视化这些音乐流派可有助于音乐探索，存档和推荐。概率主题模型在建模文本文档方面非常成功。在这项工作中，我们使用概率主题模型可视化音乐流派。与文本文档不同，音频是连续的，需要切成较小的片段。我们使用这些片段的简单MFCC功能作为音乐词汇。我们将主题模型应用于语料库，然后使用数据的体裁注释来解释和可视化潜在空间。