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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
---
```
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*](https://www.arxiv.org/abs/2407.06823).
## 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.
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