Audio Match Cutting: Finding and Creating Matching Audio Transitions in
  Movies and Videos
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/
66c5552f1fa603b9d4eff4b7
Dennis Fedorishin
66c5552f1fa603b9d4eff4b8
Lie Lu
66c5552f1fa603b9d4eff4b9
Srirangaraj Setlur
66c5552f1fa603b9d4eff4ba
Venu Govindaraju
Audio Match Cutting: Finding and Creating Matching Audio Transitions in
  Movies and Videos
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/
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.
self-supervised audio representation audio match pipeline coarse-to-fine audio match cut task audio blending methods