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Music tagging and content-based retrieval systems have traditionally beenconstructed using pre-defined ontologies covering a rigid set of musicattributes or text queries. This paper presents MuLan: a first attempt at a newgeneration of acoustic models that link music audio directly to unconstrainednatural language music descriptions. MuLan takes the form of a two-tower, jointaudio-text embedding model trained using 44 million music recordings (370Khours) and weakly-associated, free-form text annotations. Through itscompatibility with a wide range of music genres and text styles (includingconventional music tags), the resulting audio-text representation subsumesexisting ontologies while graduating to true zero-shot functionalities. Wedemonstrate the versatility of the MuLan embeddings with a range of experimentsincluding transfer learning, zero-shot music tagging, language understanding inthe music domain, and cross-modal retrieval applications.

MuLan: A Joint Embedding of Music Audio and Natural Language