MuLan: A Joint Embedding of Music Audio and Natural Language

• URL: http://arxiv.org/abs/2208.12415v1
• Date: Fri, 26 Aug 2022 03:13:21 GMT
• Title: MuLan: A Joint Embedding of Music Audio and Natural Language
• Authors: Qingqing Huang, Aren Jansen, Joonseok Lee, Ravi Ganti, Judith Yue Li, Daniel P. W. Ellis
• Abstract summary: This paper presents a new generation of models that link audio annotations directly to natural language descriptions. MuLan takes the form of a two-tower, joint audio-text embedding model trained using 44 million music recordings.
• Score: 15.753767984842014
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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