  The explosion of short videos has dramatically reshaped the manners people
socialize, yielding a new trend for daily sharing and access to the latest
information. These rich video resources, on the one hand, benefited from the
popularization of portable devices with cameras, but on the other, they can not
be independent of the valuable editing work contributed by numerous video
creators. In this paper, we investigate a novel and practical problem, namely
audio beat matching (ABM), which aims to recommend the proper transition time
stamps based on the background music. This technique helps to ease the
labor-intensive work during video editing, saving energy for creators so that
they can focus more on the creativity of video content. We formally define the
ABM problem and its evaluation protocol. Meanwhile, a large-scale audio
dataset, i.e., the AutoMatch with over 87k finely annotated background music,
is presented to facilitate this newly opened research direction. To further lay
solid foundations for the following study, we also propose a novel model termed
BeatX to tackle this challenging task. Alongside, we creatively present the
concept of label scope, which eliminates the data imbalance issues and assigns
adaptive weights for the ground truth during the training procedure in one
stop. Though plentiful short video platforms have flourished for a long time,
the relevant research concerning this scenario is not sufficient, and to the
best of our knowledge, AutoMatch is the first large-scale dataset to tackle the
audio beat matching problem. We hope the released dataset and our competitive
baseline can encourage more attention to this line of research. The dataset and
codes will be made publicly available.

Sound;Computer Vision and Pattern Recognition;Multimedia;Audio and Speech Processing