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@@ -79,6 +79,13 @@ dataset = datasets.load_dataset("jpdiazpardo/scream_detection_heavy_metal")
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  ### Youtube playlist
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  [Scream Detection Dataset](https://www.youtube.com/playlist?list=PLnkRJFUtBDzWOEnVOiWTVxGOWD70LDwtC)
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  ### Licensing Information
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  MIT License
@@ -101,4 +108,15 @@ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLI
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  year={2022},
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  volume={abs/2205.05580}
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  }
 
 
 
 
 
 
 
 
 
 
 
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  ```
 
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  ### Youtube playlist
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  [Scream Detection Dataset](https://www.youtube.com/playlist?list=PLnkRJFUtBDzWOEnVOiWTVxGOWD70LDwtC)
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+ ### Source Data
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+
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+ #### Initial Data Collection and Normalization
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+ The data was collected from the YouTube playlist above and trimmed using the timestamps provided in the dataset.
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+ The audio files were passed through the [Spleeter](https://joss.theoj.org/papers/10.21105/joss.02154) (Hennequin et al., 2020) source separation algorithm to separate the vocals from the other components.
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+
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  ### Licensing Information
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  MIT License
 
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  year={2022},
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  volume={abs/2205.05580}
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  }
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+
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+ @article{Hennequin2020,
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+ doi = {10.21105/joss.02154},
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+ url = {https://doi.org/10.21105/joss.02154},
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+ year = {2020}, publisher = {The Open Journal},
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+ volume = {5}, number = {50}, pages = {2154},
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+ author = {Romain Hennequin and Anis Khlif and Felix Voituret and Manuel Moussallam},
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+ title = {Spleeter: a fast and efficient music source separation tool with pre-trained models},
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+ journal = {Journal of Open Source Software}
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+
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+ }
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  ```