Music tagging and content-based retrieval systems have traditionally been
constructed using pre-defined ontologies covering a rigid set of music
attributes or text queries. This paper presents MuLan: a first attempt at a new
generation of acoustic models that link music audio directly to unconstrained
natural language music descriptions. MuLan takes the form of a two-tower, joint
audio-text embedding model trained using 44 million music recordings (370K
hours) and weakly-associated, free-form text annotations. Through its
compatibility with a wide range of music genres and text styles (including
conventional music tags), the resulting audio-text representation subsumes
existing ontologies while graduating to true zero-shot functionalities. We
demonstrate the versatility of the MuLan embeddings with a range of experiments
including transfer learning, zero-shot music tagging, language understanding in
the music domain, and cross-modal retrieval applications.

MuLan 是一种新型的声学模型，通过将音乐音频直接链接到自由形式的文字注释，构建与各种音乐流派和文本样式兼容的音频 - 文本表示，具有真正的零样本功能，可用于传输学习、零样本音乐标记、音乐领域语言理解和跨模态检索应用等。