title: XMusic: Towards a Generalized and Controllable Symbolic Music Generation
    Framework
summary: In recent years, remarkable advancements in artificial intelligence-generated
  content (AIGC) have been achieved in the fields of image synthesis and text
  generation, generating content comparable to that produced by humans. However,
  the quality of AI-generated music has not yet reached this standard, primarily
  due to the challenge of effectively controlling musical emotions and ensuring
  high-quality outputs. This paper presents a generalized symbolic music
  generation framework, XMusic, which supports flexible prompts (i.e., images,
  videos, texts, tags, and humming) to generate emotionally controllable and
  high-quality symbolic music. XMusic consists of two core components, XProjector
  and XComposer. XProjector parses the prompts of various modalities into
  symbolic music elements (i.e., emotions, genres, rhythms and notes) within the
  projection space to generate matching music. XComposer contains a Generator and
  a Selector. The Generator generates emotionally controllable and melodious
  music based on our innovative symbolic music representation, whereas the
  Selector identifies high-quality symbolic music by constructing a multi-task
  learning scheme involving quality assessment, emotion recognition, and genre
  recognition tasks. In addition, we build XMIDI, a large-scale symbolic music
  dataset that contains 108,023 MIDI files annotated with precise emotion and
  genre labels. Objective and subjective evaluations show that XMusic
  significantly outperforms the current state-of-the-art methods with impressive
  music quality. Our XMusic has been awarded as one of the nine Highlights of
  Collectibles at WAIC 2023. The project homepage of XMusic is
  https://xmusic-project.github.io.
paper_authors._id: 678893719b735715ac69debe
paper_authors.name: Sida Tian
paper_authors._id: 678893719b735715ac69debf
paper_authors.name: Can Zhang
paper_authors._id: 678893719b735715ac69dec0
paper_authors.name: Wei Yuan
paper_authors._id: 678893719b735715ac69dec1
paper_authors.name: Wei Tan
paper_authors._id: 678893719b735715ac69dec2
paper_authors.name: Wenjie Zhu
paper_title: XMusic: Towards a Generalized and Controllable Symbolic Music Generation
    Framework
paper_summary: In recent years, remarkable advancements in artificial intelligence-generated
  content (AIGC) have been achieved in the fields of image synthesis and text
  generation, generating content comparable to that produced by humans. However,
  the quality of AI-generated music has not yet reached this standard, primarily
  due to the challenge of effectively controlling musical emotions and ensuring
  high-quality outputs. This paper presents a generalized symbolic music
  generation framework, XMusic, which supports flexible prompts (i.e., images,
  videos, texts, tags, and humming) to generate emotionally controllable and
  high-quality symbolic music. XMusic consists of two core components, XProjector
  and XComposer. XProjector parses the prompts of various modalities into
  symbolic music elements (i.e., emotions, genres, rhythms and notes) within the
  projection space to generate matching music. XComposer contains a Generator and
  a Selector. The Generator generates emotionally controllable and melodious
  music based on our innovative symbolic music representation, whereas the
  Selector identifies high-quality symbolic music by constructing a multi-task
  learning scheme involving quality assessment, emotion recognition, and genre
  recognition tasks. In addition, we build XMIDI, a large-scale symbolic music
  dataset that contains 108,023 MIDI files annotated with precise emotion and
  genre labels. Objective and subjective evaluations show that XMusic
  significantly outperforms the current state-of-the-art methods with impressive
  music quality. Our XMusic has been awarded as one of the nine Highlights of
  Collectibles at WAIC 2023. The project homepage of XMusic is
  https://xmusic-project.github.io.
paper_ai_summary: XMusic is a framework that uses flexible prompts to generate high-quality and emotionally controllable symbolic music through XProjector and XComposer components, outperforming state-of-the-art methods.
paper_ai_keywords: symbolic music generation XMusic XProjector XComposer symbolic music elements emotions genres rhythms notes Generator Selector multi-task learning quality assessment emotion recognition genre recognition XMIDI MIDI files symbolic music representation