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.

It's a safe assumption that most DJs have the itch to create some music of their own. Obviously many of them do, but for some, the expense of new gear or the learning curve involved with the software hinders that quest. To help bridge the gap, makers of the popular Serato DJ software are releasing a new product: Serato Studio (macOS/Windows). For those who already use the company's DJ hardware and software, things will be pleasantly familiar, helping ease you into the song-making process. Of course, you don't need to be a DJ; this tool is great for anyone who wants to make music with fewer "technical roadblocks" and more creative flow.