EmoGen: Eliminating Subjective Bias in Emotional Music Generation
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/.
64a646402a782c381a7eaf95
Chenfei Kang
64a646402a782c381a7eaf96
Peiling Lu
64a646402a782c381a7eaf97
Botao Yu
64a646402a782c381a7eaf98
Xu Tan
extracted_confirmed
2023-09-03T11:25:14.984Z
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/avatars/a2f28940236ae625ed3810ad62e343ff.svg
Xu Tan
user
xutan
64a646402a782c381a7eaf99
Wei Ye
64a646402a782c381a7eaf9a
Shikun Zhang
64a646402a782c381a7eaf9b
Jiang Bian
claimed_verified
2025-06-19T09:12:32.066Z
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/avatars/4983876f615fa830cfc8f2cb5a00fa08.svg
Jiang Bian
user
jiangbian22
EmoGen: Eliminating Subjective Bias in Emotional Music Generation
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/.
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.
emotional music generation emotion labels supervised clustering self-supervised learning emotion control accuracy music quality