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Recommended to open synthetic/create_dcase2019_dataset.sh and real_data/create_dcase2019_dataset.sh and launch line by line in case of bugs.
Otherwise launch sh create_dcase2019_dataset.sh.
Description of Desed for dcase2019 task 4
Real data
- Training: 1578 weakly labeled clips, 14412 unlabeled clips
- Public Evaluation: 692 Youtube files (reported as “eval youtube” in papers)
- Challenge Evaluation: Youtube and Vimeo files.
Synthetic
- Training: There are 2060 background files from SINS and 1009 foreground from Freesound. We generated 2045 10s files with a FBSNR between 6dB to 30dB.
- Evaluation: There are 12 (Freesound) + 5 (Youtube) background files and 314 foreground files. Generating different subsets to test robustness against some parameters.Taking a background sound and multiple foreground sounds and associating them in different conditions:
- Varying the foreground-background signal to noise ratio (FBSNR).
- Varying the onsets: Generating foreground sounds only at the beginning, middle or end of the 10 seconds.
- Using long ‘foreground’ event classes as background, and short events as foreground.
- Degrading the final 10s mixtures.
After running the script create_dcase2019_dataset.sh, you should have a folder called dcase2019in that way:
dcase2019/
└── dataset
├── audio
│ ├── eval
│ │ ├── 500ms
│ │ ├── 5500ms
│ │ ├── 9500ms
│ │ ├── fbsnr_0dB
│ │ ├── fbsnr_15dB
│ │ ├── fbsnr_24dB
│ │ ├── fbsnr_30dB
│ │ ├── ls_0dB
│ │ ├── ls_15dB
│ │ └── ls_30dB
│ ├── train
│ │ ├── synthetic
│ │ ├── unlabel_in_domain
│ │ └── weak
│ └── validation
└── metadata
├── eval
├── train
└── validation
