title: Zero-Shot Unsupervised and Text-Based Audio Editing Using DDPM Inversion
summary: Editing signals using large pre-trained models, in a zero-shot manner, has
  recently seen rapid advancements in the image domain. However, this wave has
  yet to reach the audio domain. In this paper, we explore two zero-shot editing
  techniques for audio signals, which use DDPM inversion on pre-trained diffusion
  models. The first, adopted from the image domain, allows text-based editing.
  The second, is a novel approach for discovering semantically meaningful editing
  directions without supervision. When applied to music signals, this method
  exposes a range of musically interesting modifications, from controlling the
  participation of specific instruments to improvisations on the melody. Samples
  can be found on our examples page in https://hilamanor.github.io/AudioEditing/
  and code can be found in https://github.com/hilamanor/AudioEditing/ .
paper_authors._id: 65ceee613db7d7b915d25236
paper_authors.name: Hila Manor
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-02-16T10:26:58.615Z
paper_authors._id: 63f6454a9cbd673030295df4
paper_authors.avatarUrl: /avatars/90c0e4a1c5dffdabbf2492853741d0d9.svg
paper_authors.fullname: Hila Manor
paper_authors.type: user
paper_authors.user: hilamanor
paper_authors._id: 65ceee613db7d7b915d25237
paper_authors.name: Tomer Michaeli
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-16T11:10:59.321Z
paper_authors._id: 6457c3f50664b05a5b1d941d
paper_authors.avatarUrl: /avatars/0132be4d068d4b314f6b498b65411461.svg
paper_authors.fullname: Tomer Michaeli
paper_authors.type: user
paper_authors.user: Michato
paper_title: Zero-Shot Unsupervised and Text-Based Audio Editing Using DDPM Inversion
paper_summary: Editing signals using large pre-trained models, in a zero-shot manner, has
  recently seen rapid advancements in the image domain. However, this wave has
  yet to reach the audio domain. In this paper, we explore two zero-shot editing
  techniques for audio signals, which use DDPM inversion on pre-trained diffusion
  models. The first, adopted from the image domain, allows text-based editing.
  The second, is a novel approach for discovering semantically meaningful editing
  directions without supervision. When applied to music signals, this method
  exposes a range of musically interesting modifications, from controlling the
  participation of specific instruments to improvisations on the melody. Samples
  can be found on our examples page in https://hilamanor.github.io/AudioEditing/
  and code can be found in https://github.com/hilamanor/AudioEditing/ .
paper_ai_summary: Two zero-shot techniques using DDPM inversion on pre-trained diffusion models are explored for editing audio signals, enabling text-based manipulation and discovery of semantically meaningful editing directions.
paper_ai_keywords: DDPM inversion diffusion models zero-shot editing text-based editing semantically meaningful editing directions music signals instrument control melody improvisation