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The explosion of short videos has dramatically reshaped the manners peoplesocialize, yielding a new trend for daily sharing and access to the latestinformation. These rich video resources, on the one hand, benefited from thepopularization of portable devices with cameras, but on the other, they can notbe independent of the valuable editing work contributed by numerous videocreators. In this paper, we investigate a novel and practical problem, namelyaudio beat matching (ABM), which aims to recommend the proper transition timestamps based on the background music. This technique helps to ease thelabor-intensive work during video editing, saving energy for creators so thatthey can focus more on the creativity of video content. We formally define theABM problem and its evaluation protocol. Meanwhile, a large-scale audiodataset, i.e., the AutoMatch with over 87k finely annotated background music,is presented to facilitate this newly opened research direction. To further laysolid foundations for the following study, we also propose a novel model termedBeatX to tackle this challenging task. Alongside, we creatively present theconcept of label scope, which eliminates the data imbalance issues and assignsadaptive weights for the ground truth during the training procedure in onestop. Though plentiful short video platforms have flourished for a long time,the relevant research concerning this scenario is not sufficient, and to thebest of our knowledge, AutoMatch is the first large-scale dataset to tackle theaudio beat matching problem. We hope the released dataset and our competitivebaseline can encourage more attention to this line of research. The dataset andcodes will be made publicly available.

AutoMatch: A Large-scale Audio Beat Matching Benchmark for Boosting Deep Learning Assistant Video Editing