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---
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.
Kalbag, V., & Lerch, A. (2022). Scream detection in heavy metal music. arXiv preprint arXiv:2205.05580.
### Data Fields
* `audio`: the trimmed audio file from the song.
* `scream_type`: the target variable for classification i.e. layered, lowfry, highfry, midfry, clean.
* `band_name`: the name of the artist performing the song.
* `album_name`: the name of the album where the song was released.
* `song_name`: the name of the song.
* `release_year`: the release year of the song.
* `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. |