README.md

---
license: mit
size_categories:
- 1K<n<10K
pretty_name: EDM CUE
dataset_info:
  features:
  - name: id
    dtype: int64
  - name: title
    dtype: string
  - name: artists
    dtype: string
  - name: duration
    dtype: int64
  - name: genre
    sequence: string
  - name: key
    sequence: string
  - name: beat_grid
    struct:
    - name: bpm
      dtype: float64
    - name: init_beat
      dtype: int64
    - name: start_pos
      dtype: float64
    - name: time_sig
      dtype: string
  - name: cue_pts
    sequence: float64
  splits:
  - name: train
    num_bytes: 690103
    num_examples: 4609
  - name: valid
    num_bytes: 16842
    num_examples: 101
  download_size: 292711
  dataset_size: 706945
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: valid
    path: data/valid-*
tags:
- music
---

# What is EDM-CUE?

The EDM-CUE dataset contains metadata for ~5k EDM tracks, most importantly cue points manually placed by human experts.

For more information see our ISMIR'24 paper *Cue Point Estimation using Object Detection* [link coming soon].


## Dataset Structure

The dataset contains the following features:

* id: Track ID on Deezer.
* title: Track name.
* artists: Track artists.
* duration: Track duration in seconds.
* genre: List of genres (possibly incomplete).
* key: Alphanumeric key of the track generated by rekordbox (possibly incorrect).
* beat_grid: Contains 4 values to compute the grid. 'start_pos' is the starting position in seconds, 'init_beat' is the beat count of 'start_pos', 'bpm' is the beats per minute tempo, and 'time_sig' is the time signature in string format.
* cue_pts: Cue point position in seconds.