title: MusicMagus: Zero-Shot Text-to-Music Editing via Diffusion Models
summary: Recent advances in text-to-music generation models have opened new avenues in
  musical creativity. However, music generation usually involves iterative
  refinements, and how to edit the generated music remains a significant
  challenge. This paper introduces a novel approach to the editing of music
  generated by such models, enabling the modification of specific attributes,
  such as genre, mood and instrument, while maintaining other aspects unchanged.
  Our method transforms text editing to latent space manipulation while
  adding an extra constraint to enforce consistency. It seamlessly integrates
  with existing pretrained text-to-music diffusion models without requiring
  additional training. Experimental results demonstrate superior performance over
  both zero-shot and certain supervised baselines in style and timbre transfer
  evaluations. Additionally, we showcase the practical applicability of our
  approach in real-world music editing scenarios.
paper_authors._id: 65c98dfa8f6973864f79a1af
paper_authors.name: Yixiao Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-12T16:26:59.211Z
paper_authors._id: 6376a5aad49217e154f580aa
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1668720030380-noauth.jpeg
paper_authors.fullname: Yixiao ZHang
paper_authors.type: user
paper_authors.user: ldzhangyx
paper_authors._id: 65c98dfa8f6973864f79a1b0
paper_authors.name: Yukara Ikemiya
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-12T16:27:06.047Z
paper_authors._id: 62fbbdcd21c444a56f7a6159
paper_authors.avatarUrl: /avatars/3f83effd52773f0806f58331d8734861.svg
paper_authors.fullname: Yukara Ikemiya
paper_authors.type: user
paper_authors.user: yukara13
paper_authors._id: 65c98dfa8f6973864f79a1b1
paper_authors.name: Gus Xia
paper_authors._id: 65c98dfa8f6973864f79a1b2
paper_authors.name: Naoki Murata
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-12T16:27:21.604Z
paper_authors._id: 63e1e9627fbb6ae4d4f59d76
paper_authors.avatarUrl: /avatars/22197a8c85232135609331766299c3e7.svg
paper_authors.fullname: Naoki Murata
paper_authors.type: user
paper_authors.user: muttyjp
paper_authors._id: 65c98dfa8f6973864f79a1b3
paper_authors.name: Marco Martínez
paper_authors._id: 65c98dfa8f6973864f79a1b4
paper_authors.name: Wei-Hsiang Liao
paper_authors._id: 65c98dfa8f6973864f79a1b5
paper_authors.name: Yuki Mitsufuji
paper_authors._id: 65c98dfa8f6973864f79a1b6
paper_authors.name: Simon Dixon
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-12T16:28:00.696Z
paper_authors._id: 643ac28aff50448bcfc24b19
paper_authors.avatarUrl: /avatars/18d201b94829bae9985dbb4ec618e1ff.svg
paper_authors.fullname: Simon Dixon
paper_authors.type: user
paper_authors.user: Dexdixon
paper_title: MusicMagus: Zero-Shot Text-to-Music Editing via Diffusion Models
paper_summary: Recent advances in text-to-music generation models have opened new avenues in
  musical creativity. However, music generation usually involves iterative
  refinements, and how to edit the generated music remains a significant
  challenge. This paper introduces a novel approach to the editing of music
  generated by such models, enabling the modification of specific attributes,
  such as genre, mood and instrument, while maintaining other aspects unchanged.
  Our method transforms text editing to latent space manipulation while
  adding an extra constraint to enforce consistency. It seamlessly integrates
  with existing pretrained text-to-music diffusion models without requiring
  additional training. Experimental results demonstrate superior performance over
  both zero-shot and certain supervised baselines in style and timbre transfer
  evaluations. Additionally, we showcase the practical applicability of our
  approach in real-world music editing scenarios.
paper_ai_summary: Latent space manipulation allows for the editing of music generated from text, enabling attribute-specific changes while maintaining consistency, demonstrated superior performance in style and timbre transfer, and is applicable in real-world scenarios.
paper_ai_keywords: latent space manipulation diffusion models text-to-music generation genre mood instrument style transfer timbre transfer