# Cue Point Estimation using Object Detection

arXiv: https://arxiv.org/abs/2407.06823 · HTML: https://mlsanity.com/paper/2407.06823/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 9 Jul 2024.

## Builds on (most cited first, 8 of 8)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [AST: Audio Spectrogram Transformer](https://mlsanity.com/paper/2104.01778/) (2021)
- [Robust Downbeat Tracking Using an Ensemble of Convolutional Networks](https://mlsanity.com/paper/1605.08396/) (2016)
- [Automatic DJ Transitions with Differentiable Audio Effects and Generative Adversarial Networks](https://mlsanity.com/paper/2110.06525/) (2021)
- [Automatic Detection of Cue Points for DJ Mixing](https://mlsanity.com/paper/2007.08411/) (2020)
- [A Computational Analysis of Real-World DJ Mixes using Mix-To-Track Subsequence Alignment](https://mlsanity.com/paper/2008.10267/) (2020)

## Built on by (most cited first, 3 of 3)

- [Benchmarking Sub-Genre Classification For Mainstage Dance Music](https://mlsanity.com/paper/2409.06690/) (2024)
- [Barwise Section Boundary Detection in Symbolic Music Using Convolutional Neural Networks](https://mlsanity.com/paper/2509.16566/) (2025)
- [EDMFormer: Genre-Specific Self-Supervised Learning for Music Structure Segmentation](https://mlsanity.com/paper/2603.08759/) (2026)

## Abstract (arXiv)

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.