title: MuseCoco: Generating Symbolic Music from Text
summary: Generating music from text descriptions is a user-friendly mode since the
  text is a relatively easy interface for user engagement. While some approaches
  utilize texts to control music audio generation, editing musical elements in
  generated audio is challenging for users. In contrast, symbolic music offers
  ease of editing, making it more accessible for users to manipulate specific
  musical elements. In this paper, we propose MuseCoco, which generates symbolic
  music from text descriptions with musical attributes as the bridge to break
  down the task into text-to-attribute understanding and attribute-to-music
  generation stages. MuseCoCo stands for Music Composition Copilot that empowers
  musicians to generate music directly from given text descriptions, offering a
  significant improvement in efficiency compared to creating music entirely from
  scratch. The system has two main advantages: Firstly, it is data efficient. In
  the attribute-to-music generation stage, the attributes can be directly
  extracted from music sequences, making the model training self-supervised. In
  the text-to-attribute understanding stage, the text is synthesized and refined
  by ChatGPT based on the defined attribute templates. Secondly, the system can
  achieve precise control with specific attributes in text descriptions and
  offers multiple control options through attribute-conditioned or
  text-conditioned approaches. MuseCoco outperforms baseline systems in terms of
  musicality, controllability, and overall score by at least 1.27, 1.08, and 1.32
  respectively. Besides, there is a notable enhancement of about 20% in objective
  control accuracy. In addition, we have developed a robust large-scale model
  with 1.2 billion parameters, showcasing exceptional controllability and
  musicality.
paper_authors._id: 647951cc5fa05d12dc34ce76
paper_authors.name: Peiling Lu
paper_authors._id: 647951cc5fa05d12dc34ce77
paper_authors.name: Xin Xu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-02T12:46:40.922Z
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paper_authors.avatarUrl: /avatars/a677a8401360be473895494e5fb267bb.svg
paper_authors.fullname: Xin Xu
paper_authors.type: user
paper_authors.user: XinXuNLPer
paper_authors._id: 647951cc5fa05d12dc34ce78
paper_authors.name: Chenfei Kang
paper_authors._id: 647951cc5fa05d12dc34ce79
paper_authors.name: Botao Yu
paper_authors._id: 647951cc5fa05d12dc34ce7a
paper_authors.name: Chengyi Xing
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-05T12:18:02.860Z
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paper_authors.fullname: Chengyi Xing
paper_authors.type: user
paper_authors.user: xcyhbp
paper_authors._id: 647951cc5fa05d12dc34ce7b
paper_authors.name: Xu Tan
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2023-06-02T03:26:21.827Z
paper_authors._id: 5f1040b6e9d71719e3be71d2
paper_authors.avatarUrl: /avatars/a2f28940236ae625ed3810ad62e343ff.svg
paper_authors.fullname: Xu Tan
paper_authors.type: user
paper_authors.user: xutan
paper_authors._id: 647951cc5fa05d12dc34ce7c
paper_authors.name: Jiang Bian
paper_title: MuseCoco: Generating Symbolic Music from Text
paper_summary: Generating music from text descriptions is a user-friendly mode since the
  text is a relatively easy interface for user engagement. While some approaches
  utilize texts to control music audio generation, editing musical elements in
  generated audio is challenging for users. In contrast, symbolic music offers
  ease of editing, making it more accessible for users to manipulate specific
  musical elements. In this paper, we propose MuseCoco, which generates symbolic
  music from text descriptions with musical attributes as the bridge to break
  down the task into text-to-attribute understanding and attribute-to-music
  generation stages. MuseCoCo stands for Music Composition Copilot that empowers
  musicians to generate music directly from given text descriptions, offering a
  significant improvement in efficiency compared to creating music entirely from
  scratch. The system has two main advantages: Firstly, it is data efficient. In
  the attribute-to-music generation stage, the attributes can be directly
  extracted from music sequences, making the model training self-supervised. In
  the text-to-attribute understanding stage, the text is synthesized and refined
  by ChatGPT based on the defined attribute templates. Secondly, the system can
  achieve precise control with specific attributes in text descriptions and
  offers multiple control options through attribute-conditioned or
  text-conditioned approaches. MuseCoco outperforms baseline systems in terms of
  musicality, controllability, and overall score by at least 1.27, 1.08, and 1.32
  respectively. Besides, there is a notable enhancement of about 20% in objective
  control accuracy. In addition, we have developed a robust large-scale model
  with 1.2 billion parameters, showcasing exceptional controllability and
  musicality.
paper_ai_keywords: pre-trained autoencoders text-to-attribute understanding attribute-to-music generation symbolic music self-supervised attribute-conditioned text-conditioned ChatGPT large-scale model