  Cue points indicate possible temporal boundaries in a transition between two
pieces of music in DJ mixing and constitute a crucial element in autonomous DJ
systems as well as for live mixing. In this work, we present a novel method for
automatic cue point estimation, interpreted as a computer vision object
detection task. Our proposed system is based on a pre-trained object detection
transformer which we fine-tune on our novel cue point dataset. Our provided
dataset contains 21k manually annotated cue points from human experts as well
as metronome information for nearly 5k individual tracks, making this dataset
35x larger than the previously available cue point dataset. Unlike previous
methods, our approach does not require low-level musical information analysis,
while demonstrating increased precision in retrieving cue point positions.
Moreover, our proposed method demonstrates high adherence to phrasing, a type
of high-level music structure commonly emphasized in electronic dance music.
The code, model checkpoints, and dataset are made publicly available.
