  Segmenting dance video into short movements is a popular way to easily
understand dance choreography. However, it is currently done manually and
requires a significant amount of effort by experts. That is, even if many dance
videos are available on social media (e.g., TikTok and YouTube), it remains
difficult for people, especially novices, to casually watch short video
segments to practice dance choreography. In this paper, we propose a method to
automatically segment a dance video into each movement. Given a dance video as
input, we first extract visual and audio features: the former is computed from
the keypoints of the dancer in the video, and the latter is computed from the
Mel spectrogram of the music in the video. Next, these features are passed to a
Temporal Convolutional Network (TCN), and segmentation points are estimated by
picking peaks of the network output. To build our training dataset, we annotate
segmentation points to dance videos in the AIST Dance Video Database, which is
a shared database containing original street dance videos with
copyright-cleared dance music. The evaluation study shows that the proposed
method (i.e., combining the visual and audio features) can estimate
segmentation points with high accuracy. In addition, we developed an
application to help dancers practice choreography using the proposed method.
