title: Audio Match Cutting: Finding and Creating Matching Audio Transitions in
    Movies and Videos
summary: A "match cut" is a common video editing technique where a pair of shots that
  have a similar composition transition fluidly from one to another. Although
  match cuts are often visual, certain match cuts involve the fluid transition of
  audio, where sounds from different sources merge into one indistinguishable
  transition between two shots. In this paper, we explore the ability to
  automatically find and create "audio match cuts" within videos and movies. We
  create a self-supervised audio representation for audio match cutting and
  develop a coarse-to-fine audio match pipeline that recommends matching shots
  and creates the blended audio. We further annotate a dataset for the proposed
  audio match cut task and compare the ability of multiple audio representations
  to find audio match cut candidates. Finally, we evaluate multiple methods to
  blend two matching audio candidates with the goal of creating a smooth
  transition. Project page and examples are available at:
  https://denfed.github.io/audiomatchcut/
paper_authors._id: 66c5552f1fa603b9d4eff4b7
paper_authors.name: Dennis Fedorishin
paper_authors._id: 66c5552f1fa603b9d4eff4b8
paper_authors.name: Lie Lu
paper_authors._id: 66c5552f1fa603b9d4eff4b9
paper_authors.name: Srirangaraj Setlur
paper_authors._id: 66c5552f1fa603b9d4eff4ba
paper_authors.name: Venu Govindaraju
paper_title: Audio Match Cutting: Finding and Creating Matching Audio Transitions in
    Movies and Videos
paper_summary: A "match cut" is a common video editing technique where a pair of shots that
  have a similar composition transition fluidly from one to another. Although
  match cuts are often visual, certain match cuts involve the fluid transition of
  audio, where sounds from different sources merge into one indistinguishable
  transition between two shots. In this paper, we explore the ability to
  automatically find and create "audio match cuts" within videos and movies. We
  create a self-supervised audio representation for audio match cutting and
  develop a coarse-to-fine audio match pipeline that recommends matching shots
  and creates the blended audio. We further annotate a dataset for the proposed
  audio match cut task and compare the ability of multiple audio representations
  to find audio match cut candidates. Finally, we evaluate multiple methods to
  blend two matching audio candidates with the goal of creating a smooth
  transition. Project page and examples are available at:
  https://denfed.github.io/audiomatchcut/
paper_ai_summary: A technique for automatically identifying and generating audio match cuts in videos is explored, with a focus on developing a self-supervised audio representation and evaluating audio blending methods.
paper_ai_keywords: self-supervised audio representation audio match pipeline coarse-to-fine audio match cut task audio blending methods