metadata

task_categories:
  - audio-classification
language:
  - en
dataset_info:
  features:
    - name: audio
      dtype: audio
    - name: scream_type
      dtype: string
    - name: song_name
      dtype: string
    - name: band_name
      dtype: string
    - name: album_name
      dtype: string
    - name: release_year
      dtype: int64
    - name: video_id
      dtype: string
    - name: timestamp_start
      dtype: float64
    - name: timestamp_end
      dtype: float64
    - name: sample_rate
      dtype: int64
  splits:
    - name: train
      num_bytes: 114577942.825
      num_examples: 1575
  download_size: 119156239
  dataset_size: 114577942.825
license: mit
tags:
  - music
size_categories:
  - 1K<n<10K

Dataset card for Scream Detection in Heavy Metal Music

This dataset contains the processed dataset used in the paper "Scream Detection in Heavy Metal Music" (Kalbag & Lerch, 2022) from the Georgia Institute of Technology.

This dataset contains annotations of 57 songs, distributed over 34 bands and 47 albums. The vocal events are labelled into 5 classes:

Clean (or sung vocal) Low Fry Scream Mid Fry Scream High Fry Scream Layered Vocals The label "Layered Vocals" has been applied to cases where there are examples of two or more classes present simultaneously.

Paper: Scream Detection in Heavy Metal Music

Kalbag, V., & Lerch, A. (2022). Scream detection in heavy metal music. arXiv preprint arXiv:2205.05580.

How to use

Load the dataset from huggingface in your notebook:

!pip install datasets[audio]

import datasets

dataset = datasets.load_dataset("jpdiazpardo/scream_detection_heavy_metal")

Data Fields

audio: the trimmed audio file from the song.
scream_type: the target variable for classification i.e. layered, lowfry, highfry, midfry, clean.
song_name: the name of the song.
band_name: the name of the artist performing the song.
album_name: the name of the album where the song was released.
release_year: the release year of the song.
video_id: the YouTube video id.
timestamp_start: the start time of the snippet from the full audio.
tiemstamp_end: the end time of the snippet from the full audio.
sample_rate: the sampling rate of the audio.

Youtube playlist

Scream Detection Dataset

Source Data

Initial Data Collection and Normalization

The data was collected from the YouTube playlist above and trimmed using the timestamps provided in the dataset.

The audio files were passed through the Spleeter (Hennequin et al., 2020) source separation algorithm to separate the vocals from the other components.

Licensing Information

MIT License

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Citation Information

@article{
  title={Scream Detection in Heavy Metal Music},
  author={Vedant Kalbag and Alexabder Lerch},
  journal={ArXiv},
  year={2022},
  volume={abs/2205.05580}
}

@article{Hennequin2020,
         doi = {10.21105/joss.02154},
         url = {https://doi.org/10.21105/joss.02154},
         year = {2020}, publisher = {The Open Journal},
         volume = {5}, number = {50}, pages = {2154},
         author = {Romain Hennequin and Anis Khlif and Felix Voituret and Manuel Moussallam},
         title = {Spleeter: a fast and efficient music source separation tool with pre-trained models},
         journal = {Journal of Open Source Software}

}