metadata

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

from datasets import load_dataset
captions = load_dataset("disco-eth/edm-cue")

What is EDM-CUE?

The EDM-CUE dataset contains metadata for ~5k EDM tracks. Cue points are essential for DJs, so we asked the question "can they be placed by a learned system?" To Answer this question we gathered 21k cue points manually placed by human experts, and provide them in this dataset for future use.

To cite this dataset or for more information, please see Cue Point Estimation using Object Detection.

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: Position of the cue point in seconds.