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---
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.
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