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--- |
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task_categories: |
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- audio-classification |
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language: |
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- en |
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dataset_info: |
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features: |
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- name: audio |
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dtype: audio |
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- name: scream_type |
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dtype: string |
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- name: band_name |
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dtype: string |
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- name: album_name |
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dtype: string |
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- name: song_name |
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dtype: string |
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- name: release_year |
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dtype: int64 |
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- name: timestamp_start |
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dtype: float64 |
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- name: timestamp_end |
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dtype: float64 |
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- name: sample_rate |
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dtype: int64 |
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splits: |
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- name: train |
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num_bytes: 114554317.825 |
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num_examples: 1575 |
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download_size: 119150676 |
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dataset_size: 114554317.825 |
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--- |
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# Dataset card for Scream Detection in Heavy Metal Music |
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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. |
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Kalbag, V., & Lerch, A. (2022). Scream detection in heavy metal music. arXiv preprint arXiv:2205.05580. |
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### Data Fields |
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* `audio`: the trimmed audio file from the song. |
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* `scream_type`: the target variable for classification i.e. layered, lowfry, highfry, midfry, clean. |
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* `band_name`: the name of the artist performing the song. |
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* `album_name`: the name of the album where the song was released. |
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* `song_name`: the name of the song. |
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* `release_year`: the release year of the song. |
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* `timestamp_start`: the start time of the snippet from the full audio. |
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* `tiemstamp_end`: the end time of the snippet from the full audio. |
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* `sample_rate`: the sampling rate of the audio. |
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