DCASE 2019 – Task 4

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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

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