title: EmoGen: Eliminating Subjective Bias in Emotional Music Generation
summary: Music is used to convey emotions, and thus generating emotional music is
  important in automatic music generation. Previous work on emotional music
  generation directly uses annotated emotion labels as control signals, which
  suffers from subjective bias: different people may annotate different emotions
  on the same music, and one person may feel different emotions under different
  situations. Therefore, directly mapping emotion labels to music sequences in an
  end-to-end way would confuse the learning process and hinder the model from
  generating music with general emotions. In this paper, we propose EmoGen, an
  emotional music generation system that leverages a set of emotion-related music
  attributes as the bridge between emotion and music, and divides the generation
  into two stages: emotion-to-attribute mapping with supervised clustering, and
  attribute-to-music generation with self-supervised learning. Both stages are
  beneficial: in the first stage, the attribute values around the clustering
  center represent the general emotions of these samples, which help eliminate
  the impacts of the subjective bias of emotion labels; in the second stage, the
  generation is completely disentangled from emotion labels and thus free from
  the subjective bias. Both subjective and objective evaluations show that EmoGen
  outperforms previous methods on emotion control accuracy and music quality
  respectively, which demonstrate our superiority in generating emotional music.
  Music samples generated by EmoGen are available via this
  link:https://ai-muzic.github.io/emogen/, and the code is available at this
  link:https://github.com/microsoft/muzic/.
paper_authors._id: 64a646402a782c381a7eaf95
paper_authors.name: Chenfei Kang
paper_authors._id: 64a646402a782c381a7eaf96
paper_authors.name: Peiling Lu
paper_authors._id: 64a646402a782c381a7eaf97
paper_authors.name: Botao Yu
paper_authors._id: 64a646402a782c381a7eaf98
paper_authors.name: Xu Tan
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2023-09-03T11:25:14.984Z
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: 64a646402a782c381a7eaf99
paper_authors.name: Wei Ye
paper_authors._id: 64a646402a782c381a7eaf9a
paper_authors.name: Shikun Zhang
paper_authors._id: 64a646402a782c381a7eaf9b
paper_authors.name: Jiang Bian
paper_title: EmoGen: Eliminating Subjective Bias in Emotional Music Generation
paper_summary: Music is used to convey emotions, and thus generating emotional music is
  important in automatic music generation. Previous work on emotional music
  generation directly uses annotated emotion labels as control signals, which
  suffers from subjective bias: different people may annotate different emotions
  on the same music, and one person may feel different emotions under different
  situations. Therefore, directly mapping emotion labels to music sequences in an
  end-to-end way would confuse the learning process and hinder the model from
  generating music with general emotions. In this paper, we propose EmoGen, an
  emotional music generation system that leverages a set of emotion-related music
  attributes as the bridge between emotion and music, and divides the generation
  into two stages: emotion-to-attribute mapping with supervised clustering, and
  attribute-to-music generation with self-supervised learning. Both stages are
  beneficial: in the first stage, the attribute values around the clustering
  center represent the general emotions of these samples, which help eliminate
  the impacts of the subjective bias of emotion labels; in the second stage, the
  generation is completely disentangled from emotion labels and thus free from
  the subjective bias. Both subjective and objective evaluations show that EmoGen
  outperforms previous methods on emotion control accuracy and music quality
  respectively, which demonstrate our superiority in generating emotional music.
  Music samples generated by EmoGen are available via this
  link:https://ai-muzic.github.io/emogen/, and the code is available at this
  link:https://github.com/microsoft/muzic/.
paper_ai_summary: EmoGen generates emotional music using a two-stage process that translates emotion labels to general music attributes and then generates music via self-supervised learning, overcoming subjective bias.
paper_ai_keywords: emotional music generation emotion labels supervised clustering self-supervised learning emotion control accuracy music quality